I would:

Option 1:

Independently quantify genes of each sample. --> Normalize to 10,000 reads per cell (Default in most scRNA analysis) / ScTransform --> Transform the matrix to square root (instead of log2(counts+1) --> Merge the three matrices (cbind)--> Remove genes that are lowly expressed in less than 1 or 2 or 5% of the cells --> Use combat to remove batch effects (here three batches) --> import the matrix to Seurat --> Skip Normalisation --> PCA/UMAPClustering etc... I am pretty sure the cells will be clustered by cell-types rather than samples.

If you want to see gene expression changes across clusters, I would introduce an extra step of imputation. So It would be:

Independently quantify genes of each sample. --> Normalize to 10,000 reads per cell (Default in most scRNA analysis) --> Transform the matrix to square root (instead of log2(counts+1) / ScTransform--> Merge the three matrices --> Remove genes that are lowly expressed in less than 1 or 2 or 5% of the cells --> Use combat to remove batch effects (here three batches) --> Impute gene expression (For example MAGIC) --> import the matrix to Seurat --> Skip Normalisation --> PCA/UMAPClustering etc...

Take average of gene expression for each cluster and calculate a cluster specificity score (Tau Score for example) and them take genes with Tau score more than 0.5 or 0.3 and Perform K-means clustering of averaged gene expression across clusters to pick markers.

Option 2: Use Seurat (v3) CCA analysis to integrate datasets. Straightforward. It performs SCTransform instead of Library size normalisation, which seems to be better for scRNA data but it depends on end goal.

Option 3: Or if you want to use seurat default differential analysis, start with raw counts but use the knn graph from above analysis and proceed with typical marker analysis or differential gene expression analysis.

Its all custom analysis but works pretty well and its fun.