I hope that's not true. They should say that if there is an interaction term, say between X and Z called XZ, then the interpretation of the individual coefficients for X and for Z cannot be interpreted in the same way as if XZ were not present. You can definitely interpret it.

Question 2

If the interaction makes theoretical sense then there is no reason not to leave it in, unless concerns for statistical efficiency for some reason override concerns about misspecification and allowing your theory and your model to diverge.

Given that you have left it in, then interpret your model using marginal effects in the same way as if the interaction were significant. For reference, I include a link to Brambor et al. to show how the interpretation goes.

Think of it this way: you often have control variables in a model that turn out not to be significant, but you don't (or shouldn't) go chopping them out at the first sign of missing stars.

Question 1

You ask whether you can 'conclude that the two predictors have an effect on the response?' Apparently you can, but you can also do better. For the model with the interaction term you can report what effect the two predictors actually have on the dependent variable (marginal effects) in a way that is indifferent to whether the interaction is significant, or even present in the model.

The Bottom Line

If you remove the interaction you are re-specifying the model. This may be a reasonable thing to do for many reasons, some theoretical and some statistical, but making it easier to interpret the coefficients is not one of them.

If you want the unconditional main effect then yes you do want to run a new model without the interaction term because that interaction term is not allowing you to see your unconditional main effects correctly. The main effects calculated with the interaction present are different from the main effects as one typically interprets them in something like ANOVA. For example, it's possible to have a non-significant interaction that is still substantial enough to make it look like you have main effects when you don't have main effects.

Let's say you have two predictors, A and B. When you include the interaction term the magnitude of A is allowed to vary depending on B and vice versa. The calculated magnitude of A is for when B is 0 and the interaction term is 0. But, when the regression is just additive A is not allowed to vary across B and you just get the main effect of A independent of B. This can be a very different value even if the interaction is not significant. It is the only way to really assess the main effect by itself. On the other hand, when your interaction is meaningful (theoretically, not statistically) and you want to keep it in your model then the only way to assess A is looking at it across levels of B. That's actually the kind of thing you have to consider with respect to the interaction, not whether A is significant. You can only really see whether there's an unconditional effect of A in the additive model.

So, the models are looking at very different things and this is not an issue of multiple testing. You must look at it both ways. You don't decide based on significance. The best main effect to report is from the additive model. You make a decision on including or presenting the non significant interaction based on theoretical issues, data presentation issues, etc.

(All this can be qualified by a truly insignificant near 0 interaction that does nothing whatsoever. In that case the additive and interaction models are the same. That's not something you've stated. Either way, it is NOT multiple testing to look at both.)

f the main effects are significant but not the interaction you simply interpret the main effects, as you suggested.

You do not need to run another model without the interaction (it is generally not the best advice to exclude parameters based on significance, there are many answers here discussing that). Just take the results as they are.