I agree that it could cause confusion to present two fitted models, especially since it appears that one of them may not fit the data very well. You can tell by comparing the deviance or some other fit statistics for the two models to see if adding the interaction makes an important difference overall.

The reason the significance of the effects changes is because there is multicollinearity. The interaction effects are doing a better job of explaining some of the effects that previously were explained by $X$ alone. This could happen, for example, if $X$ Is important for one group but not the others. Without the interaction, we are fitting an average X trend to the groups, which could be significant by virtue of the one group where it is significant. I'm not saying that's exactly what is happening, just that that is one plausible scenario.

What you can do to explain the one model is to PLOT the fitted lines for each group! Then you can see what the model is describing. You could even go a step further and apply the inverse logit to these lines to show the estimated probability for each group, versus X. 

Results with and without interaction
As a part of a input validation I was thinking whether this is a really valid cron expression and how it would executed:

0 0/0 * * * ?


Quartz validation returns true

org.quartz.CronExpression.isValidExpression("0 0/0 * * * ?")


So, does this run all the time, never, every hour or every minute...?
