I'm going to propose two plausible explanations. But first some basis.
Calculation and Abstract Mathematics are not the same kind of mathematics.
Calculation is the memorization and application of algorithms on numbers. It is about speed, accuracy, and is best done by getting your conscious mind out of the way.
Abstract Mathmatics is understanding logical relationships between descriptions of ways to think about patterns and the implications. You can get to the point where your conscious mind is not in the way here, but that is a long way down the road.
Dyscalculia is a learning disorder with being able to learn how to calculate, at least as taught in school. Schools for the most part teach calculation via memorization, cargo cult tricks, and practice. You memorize addition and times tables.
Students are traditionally tested on your speed and accuracy of adding numbers together. How well they can produce the numerical result, and then problems that stack multiple such calculations on top of each other. How this relates back to a formal or abstract definition of addition is unimportant to the testing criteria.
Only rarely are logical relationships tested, like "if you add up positive numbers, can the result be negative?", or "you have a large number of negative numbers you are multiplying together. Is there an easy way to tell if the result will be negative or positive?"
The even more abstract topics -- set theory, groups, rings, fields -- also aren't mainly about calculation. There are very few algorithms you have to memorize and practice and get perfect in order to reason about these topics.
And while you can memorize facts and arguments (or proofs), abstract mathmatics evaluation isn't a series of "produce a proof for this fact" repeated dozens or hundreds of times, where each proof is nearly identical and can be mechanically produced from the fact. The strategies used in calculation aren't very effective.
Instead, what you get in abstract mathematics are patterns of arguments, logical connections. Each abstraction is supported from both above and below - from above, what the abstraction connects to more concretely, and from below by the arguments (proofs) and definitions about how it works
Things you know from above (the concrete uses) and below (the axioms and rules) are related logically and can be used to fill in gaps in your knowledge (error correction). Arguments and proofs are related to this, in that producing an argument or proof is like filling in the holes in a story.
So abstract mathematics is full of reasoning; but none of it is "how fast can you learn an algorithm and reliably execute it". The relation to dyscalculia (and calculation) is quite remote.
Based on this, we shouldn't expect one's skills at Calculation and Abstract Mathematics to be super tied to each other. There might be a common skill that boosts both, or a general intelligence factor.
Now, the students are in the same level of mathematics education. This means neither has been thrown out of class and placed in remedial education, and possibly students that fell behind where given extra attention to catch up.
This acts as a filter on the sample set of people; the kids have a "minimal level of math skill" to be in a class.
Berkson's paradox means that if there are two ways to qualify, they will anti-correlate within such a population. Being skilled at running algorithms and having the ability to handle math manually are two ways to solve math problems; so in a math classroom with any filtering at all, they'll be anti-correlated if they are uncorrelated without that filter.
The second possibility is an actual advantage for the dyscalculic. Students who are good at Calculation are going to learn how to train their own brain to shut off the conscious mind when doing these tasks. The initial "drill" period is the only part where they have to consciously work at the task, and these initial "drill" periods are designed to reduce cognitive load for the learning student. Then the next level of calculation is added, which assumes the previous level has been in-brain automated.
Students who have difficulty automating the calculation tasks are instead forced to do them manually in their conscious mind. This is going to be a lot more work in practice. But the practice should give them a larger working memory and teach them how to handle lots of facts and information at once. The error rate is still going to be much higher.
Faced with abstract mathematics, the calculation skilled students will discover that their ability to learn algorithms fails them. Their approach won't work. They are forced to keep using their conscious minds, something they are not used to having to do in their math education, and on problems that aren't spoon-fed for ideal practice.
The students not skilled at calculation are also forced to use their conscious minds, but they have been doing so for all of their math work already. They are used to having to think instead of shutting their brain off and letting the numbers fall out. Their working memory may be better, their coping strategies work better.
Before, their error rate and slow speed when doing 7234*23+234 meant that they evaluated poorly. But in this new context, making a small logical error in a 9 input factor logical argument is evaluated as a good job. And if their error correction strategy was to look around and see if it makes sense, they can find that error and remove it.
The calculation skilled student is used to 7234*23+234 type problems, and solves them by following the rigid set of rules for solving them, and does so fast and reliably. Their conscious mind is only observing the steps. When faced with a 9 input factor logical problem there is no such rigid set of rules to follow to solve it, and they haven't practiced the same argument 100s of times before so they can't just grind it out.
So it is plausible that they could have an advantage, given the different way to approach problems.