If $X1$ and $X2$ are almost perfectly correlated (but variance of x1 is a million times variance of x2) so that the first (normalised) pca factor is (1/sqrt(2),1/sqrt(2)) and second is (1/sqrt(2),-1/sqrt(2)), they are orthogonal.
However if you replace the normalised assets in eigenvectors by NON normalised assets, they are now correlated because x2 is insignificant in comparison to x1. Infact they will now be perfectly correlated.
There is absolutely nothing funny going on, it's just how the data is.
If you create factors out of non scaled returns, you are (mostly) ranking assets in terms of their variances.
Thus your factors are (mostly) assets themselves. If you take k of these, k of your regressions will have perfect R squares. Again this is not surprising.
In the other case, you changed your PCA factors to un-normalized, and they no longer reflect the "PCA" decomposition of your real returns. So poorer performance is expected, as these are not the factors that govern real data. In other words, by rescaling the eigen vectors, you drowned out the assets with small variances, and no wonder you will not be able to explain the real returns of these assets.