This means that your data is non-linear.
PCA is a dimensionality reduction (or data visualization) tool that assumes a linearity in the data. On the other hand, t-SNE and UMAP are non-linear visualization tools. They allow to visualize data in a lower dimensional space withouth losing so much information and without assuming any linearity.
Thus, this basically means that your data is non-linear. Don't be afraid, because most of the data is non-linear, just take it into account.
P.D: I always use UMAP over t-SNE. In my experience, it offers better results and it's less stochastic in some way. Furthermore, in UMAP you can prudently trust in clusters size and clusters distances, but in t-SNE you cannot trust in these measures.