For the LASSO (and other model selecting procedures) it is crucial to rescale the predictors. The general recommendation I follow is simply to use a 0 mean, 1 standard deviation normalization for continuous variables. But what is there to do with dummies?
E.g. some applied examples from the same (excellent) summer school I linked to rescales continuous variables to be between 0 and 1 (not great with outliers though), probably to be comparable to the dummies. But even that does not guarantee that the coefficients should be the same order of magnitude, and thus penalized similarly, the key reason for rescaling, no?