Many subjects deal with the effect of the correlation of explanatory variables that can be negative if the correation between the two variables is too high (multicollinearity)

Indeed this can inflate the p.value or in any case make the slope coefficients of the regressors unstable.

My question is the following:

I explain the context:

I made a multiple linear regression with 1 quantitative explained variable and 2 quantitative explanatory variables correlated to 0.57. So on R : lm (y ~ X1 + X2).

Someone would explain to me why when I switch the input order of the variables like this:

lm (y ~ X1 + X2)

lm (y ~ X2 + X1)

the estimated coefficients are the same and do not move while one would expect them to fluctuate because of the collinearity of the factors X1 and X2?

Thank you very much