Random Forest Model Giving Same Accuracy for different feature sets after tuning
The fact that their accuracy is the same but their AUC score is different is encouraging. First and foremost, it would be good for you to inspect what is learned by the various trees. A good way to do that is to follow the instructions on that link.

What I think is that all 3 algorithms essentially learned that the same features are useful and that these features are present in each feature set. That is what random forests do, given a lot of features, they inherently "decide" which features are worth using.