PCA & Clustering Confusion
My answer would be second option
I think the use of PCA is to represent original high dimensional information/data in lower dimension by calculationg the direction/axes along which there is maximum variablity in data.
In first case, where you filter for 0-labeled observations and then do PCA so PCA would measure variablity based on a smaller version of original data, and would make different axes than the second case, where the PCA would measure variablity accross entire data and hence the axes made in second case could be different. So in the first option the dataset you get after PCA is not a correct representation of high dimensional 0-labeled observations.