How to give a higher importance to certain features in a (k-means) clustering model?
You cannot really use k-means clustering if your data contains categorical variables since k-means uses Euclidian distance which will not make a lot of sense with categorical variables. Check out the answers to this similar question.

You can use the following rules for performing clustering with k-means or one of its derivates:

If your data contains only metric variables:

Scale the data and use k-means (R) (Python).

If your data contains only categorical variables:

Use k-modes (R) (Python).

If your data contains categorical and metric variables:

Scale the metric variables and use k-prototypes (R) (Python).