Random Forest but keep only leaves with impurities below a threshold
If some leaves are pruned then it means that the model cannot predict the instances which would normally have fallen into these leaves. The logic of a decision tree is to represent every possible instance: like any supervised learning method, it must be able to predict for every possible instance as input. In some cases where all the leaves have low purity, this condition would even make the model unable to predict anything at all. This is why there is no such algorithm, at least not as a regular supervised learning algorithm.
However you can rely on the probability predicted for the instance instead, this gives you exactly the same information in a way consistent with standard ML.