Could you try to use these features separately first and check the performance of the models?
Also, could you check the feature statistics (mean, min, max) of both feature sets?
After this I would compare the “single feature models” with the combined one and check, if the performance differs a lot or if the combined model might just learn from one feature while ignoring the other.
If you have the feeling (I’m not sure how to check it mathematically other then maybe low weights for the ignored feature) that this might be the case, I would try to add some normalization layers before combining the features, so that the range will be at least closer to each other.
Also, a good baseline using these combined features might be an XGBoost model to compare against (if you can use other methods besides neural networks).