What affects the magnitude of lasso penalty of a feature?
When you increase the scale of $X_1$ by 10, the scale of the corresponding weight in you linear regression sees its scale divided by 10 (it is exactly 10 for standard linear regression and the same order of magnitude holds for lasso loss). As a consequence, the $L_1$ penalty for this coefficient is also divided by a factor 10 and it is less likely to be set to 0.