When adding polynomial features, the issue of multicollinearity doesn't hold. Why? In the regression model, sometimes to capture the non-linear relationship between dependent and independent variables, we use polynomial features. But in regression, if the two or more features are correlated with each other, then regression coefficients become unstable, and non-interpretable, Causing the standard error of the model unreliable. But such a problem doesn't occur when we use polynomial features. Why is this the case?

The premise of your question seems arbitrary to me, and you give no source to back this assertion.  As far as I can see, there is no reason that the use of polynomial regression terms would remove instability in estimation due to multicollinearity.
