If the SEM model specifies all the same "cause" and "effect" paths linking the (latent) variables as are implied by a regression/mediation model, then the regression results are pretty much redundant. The researcher might choose to include the regression results if such results are more likely to be familiar to, and understood by, the audience for the report than SEM would be.

Sometimes, SEM models only include linkages for the indicators of each latent variable separately, with those results being used to inform the construction of multiple-indicator scales for the theoretical variables. In that case the regression modeling is done directlyl on the (observed) scales, and hence the results do not correspond to anything in the SEM.

Burke D. Grandjean has already given a detailed explanation above, but to add to what was mentioned.

It is very important to note that the necessity of conducting regression analysis or mediating variable tests alongside structural equation modeling (SEM) depends on one's specific research objectives. SEM offers a holistic view of complex relationships, while regression analysis provides detailed insights into specific variable associations. On the other hand, mediating variable tests reveal underlying mechanisms. Combining these methods enhances the depth of analysis, allowing researchers to explore both direct and indirect effects, as well as pathways within the model.

Thus, the choice depends on the research question's complexity, with researchers opting for multiple techniques to achieve a comprehensive understanding of relationships and mechanisms in their specific study context.

I hope this helps, Tayebeh Abbasnejad I can recommend you literature that can help you understand the underlying discussion in a more detailed manner if you wish.