Deep neural network models merging
The concept of Ensemble Learning is very applicable to your scenario: given two machine learning models that can predict well (or even just better than a random choice), combine the outputs of both models (with another ML model) to produce a unified model (let's call it Model C) that is more predictive than the individual models alone.
A straightforward application to your scenario could involve using the outputs of Models A and B along with some additional features from the original feature set as inputs to Model C. This larger set of features would allow for model C to learn for example when to the signal from Model A is more informative than B based on the current context provided by additional features from the original set.
It may also be possible to do this within a deep learning framework by adding both models into the same graph and adding a layer that ensembles the signals from the two models for an output prediction.