When do we scale features and should it be done to label encoded features?
You should not use Label Encoding for Categorical data unless there is a known ranking and that also in the specified ratio between the level values.
In this case, the model will assume 10 as 2 times of 5.

One-hot will add a lot of dimensions as I can see in your data.

You must try other Categorical encoding techniques esp. Sum Coding Or Helmert.

You should also try Dimension reduction techniques post-One-Hot to see if it reduces the dimensions.

Please do read these references - 
Beyond-one-hot
Library
Kaggle tutorial
Feature Engg