I see negative silhouette scores when I have a segmentation that's really not working well. If your categorical variables just aren't very related to your metric variables, then it's possible for the metric variables by themselves to produce high quality clusters and for the categorical variables by themselves to produce high quality clusters while combining the two together produces poor clusters. I've seen this happen often enough that I suspect this is what's going on in your data.
Did you include in the mixed ensemble several solutions from the metric data and several from the categorical data? I would probably do that rather than have an ensemble of the one best metric solution and the one best categorical solution.
I imagine you may already have done this but I'd run some correlations to see how related are your categorical and metric variables. I suspect they'll be low, and if they are there's not a lot you can do to create decent segments - any algorithm you try will starve from lack of useful input to work with. If they're not so low, maybe next you could try using the Gower distance metric (works for both metric and categorical variables and it's available in the R package "daisy").