How to combine categorical and continuous input features for neural network training
There's three main approaches to solving this:


Building two models separately and then training an ensemble algorithm that receives the output of the two models as an input
Concating all the data into a single vector/tensor as a preprocessing step and then train a simple single input NN
The multiple input NN architecture you proposed


The ensemble approach is the most straight forward option and will yield decent results, however, it will not work as well as the option you proposed as because the ensemble network only receives class probabilities from the two networks as an input and will in comparison to your approach miss out on more complex relationships between the data types.

The second approach is in theory not that different from your proposed approach, with the difference being that it assumes that the network will on it's own figure out that the input consists of two types of data (as they are both in the same vector/tensor). It will take a lot of training time for the network to learn that and you might get stuck in a local minima before that even happens.

Based on my personal experience, the network you proposed is the best option as and it's likely to have shortest train time and once you get the architecture right you will find it very easy to train and maintain (retrain) the network in production as well as you will only ever have to retrain one single model.