The main difference between t-SNE and UMAP is the interpretation of the distance between objects or "clusters". I use the quotation marks since both algorithms are not meant for clustering - they are meant for visualization mostly.
t-SNE preserves local structure in the data.
UMAP claims to preserve both local and most of the global structure in the data.
This means with t-SNE you cannot interpret the distance between clusters A and B at different ends of your plot. You cannot infer that these clusters are more dissimilar than A and C, where C is closer to A in the plot. But within cluster A, you can say that points close to each other are more similar objects than points at different ends of the cluster image.
With UMAP, you should be able to interpret both the distances between / positions of points and clusters.
Both algorithms are highly stochastic and very much dependent on choice of hyperparameters (t-SNE even more than UMAP) and can yield very different results in different runs, so your plot might obfuscate an information in the data that a subsequent run might reveal.
Good old PCA on the other hand is deterministic and easily understandable with basic knowledge of linear algebra (matrix multiplication and eigenproblems), but is just a linear reduction in contrast to the non-linear reductions of t-SNE and UMAP.