How to conduct SEM on multiple datasets resulting from MI?

The long and short of it is that you would fit your model(s) to each imputed dataset. Your overall point estimates would be the averages of each dataset's vector of point estimates. The overall repeated-sampling covariance of the point estimates would be the sum of two matrices. The first matrix is the average of the datasets' repeated-sampling covariance matrices; this represents the average "within-dataset" estimate of sampling variability. You would create the second matrix by subtracting the average point-estimate vector from each datasest's point-estimate vector, taking the outer product of that difference vector with itself, and averaging those outer products across datasets. This second matrix represents the "between-dataset" sampling variability. Once you have the overall repeated-sampling covariance matrix, you can (for instance) get standard errors by taking the square roots of its diagonal elements. This paper by Don Rubin might be useful to you.

I don't know that much about MI, especially in a SEM context, so I can't say much more than that. I would imagine that you could likewise average model-fit indices, but there may be interpretational complications I'm not aware of.