So it really depends on what you use for clustering. In this case, I'm clustering by the original embeddings so the UMAP results are different. I've also seen:

1- Clustering by UMAP. Here the plot would show clean separation of topics. But the clustering algorithm would be working on highly compressed data (from the 1024 dimensions of the embedding down to the 2 of UMAP).

2- BERTopic's approach of doing UMAP down to 5 dimensions, using this dimensionality for clustering, then UMAP again from 5 to 2. Which is an interesting approach.

I've heard people having good results with all three. It's kinda hard to objectively compare, but my leaning was to give the clustering algorithm the representation containing the most information about the text.