How To Treat Features Of Different Types

Hello there, I have a medical dataset in which some features are numeric, while others are categorical. With “categorical” I mean that these features are natively encoded with ordinal integer encoding, such that every possible value is represented as an incremental integer value. It is important for you to know that this dataset has been obtained as part of a survey, so that every categorical value is referred to different types of answers such as “never”, “sometimes”, “a lot of the time” and so on. I have to apply a MLP to this kind of data and I know that in order to do it I first need to scale data. Question is, do I have to scale all features without regard to categorical ones or do I need to scale only numerical variables applying One-hot encoding to the others? I was also wondering if it is necessary to apply one-hot encoding to categorical columns or if I can leave them as they are, applying standardization only to the numerical variables.