since you have conducted a compression (and probably not all variance were explained by that 2 features) you lost some information and cannot exactly reconstruct the original features but of course you can transform your data back to the original space: Feel free to take a look at inverse_transform() at sklearn.decomposition.PCA — scikit-learn 1.3.2 documentation
Also in this thread a PCA reconstruction was discussed, see also this repo w/ mnist dataset.
You could also check the cumulative variance which is explained by:
see also this repo. I would expect that PC1 has better clustering capabilities than PC2 and the residual information gain per PC would decrease if you would use more features.
Hint: did you already conduct an elbow analysis or did you calculate a silhouette score of your clustering problem, see also this blog post?
Feel free to add a plot and also some more context regarding the problem you are solving.