The need for Bonferroni correction does not follow hard and strict rules but calls for weighing up each time you consider it.
Each time you conduct a statistical test there is a risk for an alpha error and a beta error, type I and type II error. Sometimes making an beta error is bad, because if you fail to proove a drug is efficient, you might fail to hand it to a patient who should really take it. Sometimes making an alpha error is bad, because a patient suffers from the side effects of a drug, she should not have taken in the first place.
You need to consider both, the consequences of an alpha and those of an beta error and then you will have to weigh that, not in a statistical but in a human way. When you or a sensible reader of your work comes to the conclusion, that the alpha error is the big problem that needs to be controlled at the cost of a beta error, then Bonferroni correction of Bonferroni-Holm correction will have your back. However it comes at a price as it is correcting only against the overall alpha error and thus applying a Bonferroni correction may sometimes be not indicated or even unethical, even in situations where common rules may advise its use.
In a world, where p < .05 is generally considered a success of the researcher andthe researcher feels a need to defend against the accusation of obtaining that success surreptitiously it is easy to always recommend alpha-correction but we should really not live in such a world.