Using residuals as predictors to adjust for effects of covariate in regression

I was wondering if this is appropriate:

I have two predictors that are highly correlated and collinear, x1 and x2, and are both associated with Y. I also have other covariates that I would like to include in the model. As a way of "adjusting" the association between Y and x1 for x2, I was wondering if it's reasonable to take the residuals from Y = x2 and the residuals from x1=x2, and run a regression as Y_residuals = x1_residuals + covariates.

It seems to me this is kind of like calculating partial correlations, but adding additional covariates afterward. I'm a little concerned this may introduce bias since I'm not "adjusting" the other covariates for x2, as would be the case with Y = x1 + x2 + covariates; but I was trying to get around the collinearity between x1 and x2 and I'm really only concerned with adjusting the association between x1 and Y for x2. I don't really want to get into PCA, since I think that might muddle the interpretation and x1 and x2 likely have similar and independent biological activity (as well as influencing each other). Ideally I'd perform a validation study for regression calibration, but that isn't an option so I'm trying to do the best with the data that's available. I'd greatly appreciate any suggestions or references.