Distribution of error values in linear regression vs logistic regression Why do error values in linear regression have to be normally distributed and why not in logistic regression?
Lets clarify with a simpler question


  Why points on a circle must be equally distanced from center, but
  not points on a square?


Because of how circle and square are "defined". The same goes for linear and logistic regression, we cannot pose a "why?" question when errors are "defined" in a certain way (or a better word here "assumed"). We may design a new version of linear regression by replacing Normal distribution with some other distribution, and then proceed to derive a formula or algorithm for estimating the parameters. Then use it in a real-world scenario to see how it works, and so on.

Also here is a list of good posts on stats.stackexchange.com related to characteristics of those errors:


Error distribution for linear and logistic regression,
Logistic Regression - Error Term and its Distribution.