Remove correlated features before or after splitting test and training set?
By principle in supervised ML any decision which affects the model should be made using only the training set, in order to avoid data leakage. Following this principle requires the training/test split to always be done first.
Quite often the decision seems minor and has little chance to cause data leakage, like in your case. It's tempting to make our life easier and use the whole data for some pre-processing like this. However it's a good idea to follow this principle as strictly as possible not because the effect of data leakage would be huge, but because it would be impossible to detect it if it happens: when it happens, data leakage means that the performance on the test set is biased (overestimated), and there's simply no way to know by how much (except by collecting a new test set, of course).
Your concern that the split of the data could impact the analysis of correlation is a good example of the problem: suppose there would be a difference indeed. This means that there is data leakage, since the features are different depending directly on the test data. In this case the evaluation on the test set is likely to be biased.
Another way to explain the same idea is this: if something happens or doesn't happen in the training set by chance, then it's important to see if the model is able to focus on the real patterns and ignore the noise. The only way to obtain a reliable answer to this question is if nothing in the model depends on the test set.