Should I change the dataset to one with less zeros on the independent variable? I'm running a spatial econometrics model and one of my explanatory variables is sewage coverage, which is 0 for 42% of the cities in the country of analysis. I've been told to reduce the focus of the study to a richer part of the country, where sewage coverage would have less zeros and therefore reduce bias in the estimators. Is it really a problem?

If you have unbalanced data, e.g. you have only a few cities with high sewage coverage and many with low coverage, many models might "concentrate" on the majority situation and will give poor results for the minority. But different models are differently prone to this effect, so it depends on what model you are using. You can find lots of sources out there on how to deal with unbalanced datasets, and there are also libraries that you might consider using.
But, of course, make sure that reducing your dataset as you have been told is not creating more imbalance than you already have.
