Using binary dummy to control excess of zeros in continuous data I'm running an OLS and one of the explanatory variables accounts the sewage coverage, which is zero in 42% of the observations (these are true zeros). I'm worried that the linear effect is not going to be captured very well. Does it make sense to insert a dummy in the model that equals 1 if the sewage coverage is nonexistent and 0 if it exists?

This is a reasonable way to proceed if you know that these are true 0 values that make the corresponding continuous value meaningless. This answer from @whuber illustrates how to proceed in such a situation, where loan values are used as continuous predictors but are necessarily 0 if there is no loan at all. In this scenario, it might be easier to interpret the results if you do this with a reversal of your dummy variable.
If these are true 0 values on a continuous scale with actual values near 0, then it might make more sense to use a flexible regression spline or generalized additive model to capture the potential nonlinearity.
If these aren't true 0 values but represent values below some detection limit, see this page for suggestions on how to handle left-censored predictor variables.
