Multiple XGBoost models or just 1 for a cetain type of category?
This is the main advantage of ML: if the variable has any predictive value (that is not included in another variable), it should be used by the model. So, generally speaking it doesn't really make sense to handpick your variables to make different versions of your model (however it make sense to handpick some you want to throw). That would just be equivalent to picking the first binary split in your first tree... you are not achieving much with that.
Edit: ok, it seems that the target is not really well defined as you aggregate things that are monthly payments and things that are the house value. In that case it make sense to have two models. (Honestly it would even make more sense not to aggregate those two distinct dataset in the first place).