Low number of inputs compared to outputs (per row) in neural network
1.) Perform standard preprocessing for you input data, i.e. remove mean and scale to unit variance. 

2.) No, you should train everything together. Although it is at the moment not clear, there can be relevant relationships between your outputs.

3.) You can actually use any architecture. Simply start with Dense-NN. More important is how you arange your outputs. You should have different activation and loss functions for your different outputs. For your binary outputs use (binary)-Crossentropy and for your real values use Mean Squared Error (or sth. similar). Your final output consists of course of the sum of your single losses.