Using SMOTENC in a pipeline
The usual normalisation for Euclidian distance is NOT to scale each input to unit length, but to scale each column to mean 0 and variance 1. The scaling of each data is possible but it is not common.

I dont know

The whole point of SMOTENC is not to do the one-hot encoding. One hot encoding is a way to transform categorical data into numeric data (on multiple dimensions) for algorithms that cannot deal with categorical data. SO my suggestion is not to convert the categorical columns and let SMOTENC deal with them

The Pipeline in imblearn does the right thing - it only applies the oversampling (of other imbalance strategies) on the training set not on the test set. See this question in StackOverflow: https://stackoverflow.com/questions/63520908/does-imblearn-pipeline-turn-off-sampling-for-testing