Heterogeneous clustering with text data
TL;DR

Pass the data twice in HDBSCAN.


Cluster based on the tag using word embeddings and cosine distance.
Sub-cluster the cluster from step 1 with your existing method (using the remaining 12 features).


Explanation

I suggest you do it in two steps and not give a weight to the tag feature, because the distance metric used is different. For word-embeddings, you will need to use the cosine distance between embeddings. Where as for the other 12 features you are currently using another distance (Euclidean I assume).

The first step should cluster your data on the semantical characteristic of your tag. This should cluster things like fruits, meats, vegetables, pastas...

Then, the second step can sub-cluster the data with your other 12 features. However, given your example


  cluster 1: Banana, Apple
  
  cluster 2: Pasta, Spaghetti


I don't see why this second step is necessary. You could instead of clustering a second time, just use the 12 features for ordering the data points for the purpose of your exercise. E.g. getting the top "fruits", as clustered at step 1, that people "enjoy" the most.