Feature Importance based on a Logistic Regression Model
No, you do not need to re-scale the coefficients. To the contrary - if they are scaled, you can use them as a way to compare feature importance.

Let's assume that our logistic regression model has coefficients {$ a_i$}, relating to the different (scaled) variables {$x_i$}. 
A change of $\Delta x_i $ in the variable $ x_i $  will result in an increase (or decrease, if $a_i$ is negative) of $ a_i \Delta x_i $ in $ log({\hat p_i \over {1-\hat p_i}}) $, i.e. the logit function of $ \hat p_i $, where $ \hat p_i $ is the predicted probability that the i-th example is in the positive class.

So, if the variables are scaled, you can say that if $ a_i$ is larger, then $x_i$ is more important in the model.