Feature scaling can be crucially necessary when using distance-, variance- or gradient-based methods (KNN, PCA, neural networks...), because depending on the case, it can improve the quality of results or the computational effort.
In some cases (tree-based models in particular), scaling has no impact on the performance.
There are many discussions out there about when one should scale their features, and why they should do it. Apart from interpretability (which is not a problem as long as the scaling can be reverted), I'm wondering about the opposite: are there cases when scaling is a bad idea, i.e. can have a negative impact on model quality? or less importantly, on computation time?