Does K - Means clustering on data reduced using PCA and the original data make any difference?
But can I assign the cluster labels from the pca reduced data to the original data ? would it be a right approach ? I guess not.


Yes, that is totally the right approach. Principal components are just the linear combinations of your original features that explain the most variance, so you can definitely use them for clustering. Moreover, since you only kept 2 of them you also removed a lot of noise from your data - since K-means is based on distances, the fact of having 70 features can be problematic, as you're equally weighting distances on useless features and distances on important ones. 
Summing up - yes, assign the cluster based on the results of Kmeans you obtained on the Principal Components. 
Unfortunately I cannot help you with the implementations, but remember then when mixing categorical and continuous variables you need to define an appropriate distance metric between your points. And any good implementation should allow you to do so for categorical only or continuous only data. Not sure about the mix.