SMOTE vs SMOTE-NC for binary classifier with categorical and numeric data
You have to keep in mind that machine learning is still largely an empirical field, full of ad-hoc approaches that, while they happen to work well in most cases, they lack a theoretical explanation as to why they do so.

SMOTE arguably falls under this category; there is absolutely no guarantee (theoretical or otherwise) that SMOTE-NC will work better for your data compared to SMOTE, or even that SMOTE will perform better compared with much simpler approaches, like oversampling/undersampling. Quoting from section 6.1 on SMOTE-NC of the original SMOTE paper (emphasis added):


  SMOTE-NC with the Adult dataset differs from our typical result: it performs worse than plain under-sampling based on AUC. [...] even SMOTE with only continuous features applied to the Adult dataset, does not achieve any better performance than plain under-sampling.


The authors proceed to offer some possible explanations as to why they see such not-typical performance with SMOTE/SMOTE-NC on the said dataset, but as you will see this has to do with a deep focus on the dataset itself and its characteristics, and it is itself rather empirical in nature and hardly "theoretical".

Bottom line: there is not really much to be explained here regarding your question; any further detail will require going deep with the specific characteristics of your dataset, which of course is not possible here. But I would suggest to not bother, and continue guided by your experimental results, rather than by any (practically non-existent) theory on the subject...