It is very rare to use aggregate logit for your final market simulation model and for the final utility results. Most of the time, aggregate logit is a tool for quickly assessing the basic preference structure for the market as a whole and for getting a quick look at the overall precision of the model. Indeed, it sounds like you are moving from pure aggregation toward a segment-based model (latent class analysis).

Usually, interactions that are seen in aggregate logit models tend to go away once you conduct HB analysis (individual-level estimation), since most of the time those interaction effects seen in the aggregate model are due to unrecognized heterogeneity.

Now, latent class analysis is in the middle between aggregate models and individual-level models (in terms of modeling heterogeneity). If you have a fairly low-dimension latent class solution (such as 2 to 4 groups), it's quite probable that the interaction effects (seen as significant in aggregate logit modeling) could provide significant fit to the latent class model that would be useful for out-of-sample predictions. But, if we have high-dimension latent class solutions, such as 8-10 classes or more, then my guess is that the additional heterogeneity captured in the main effects across the multiple latent class segments may near fully explain the interaction effects in many if not most cases.

Fitting interactions is a tradeoff between fitting vs. overfitting the data. Although an interaction may lead to p<0.05 in terms of internal fit to the data, the big question is whether adding the interaction effect to the model would improve predictions for new data situations (out of sample predictions, such as predicting what real customers would buy in the real marketplace).