"help" decision tree by tying 2 features together
One way is to create new feature by combining A and B by a function and eliminating A and B from dataset. The less features are correlated the better. Then you don't have redundant features.

But actually, it may be enough to use Random Forest. Then the model will have enough trees to figure out that the best split is first B and then A.