As someone who spent a decade working on databases and scalability at multiple social networks (including FB), I believe this problem is substantially more difficult than you're taking into consideration here.
Viewing a couple pages of my news feed randomly just now, I see content from a mix of friends in the U.S., Ireland, Germany, the U.K., and Hong Kong. The content is algorithmically ranked and sorted, and then paginated. Similar situation with viewing comments on popular pages, which may span many countries.
If ranking/sorting is performed either client-side (your option A) or even in a nearby aggregation server (B), each and every user-facing pageload of N posts/comments/whatever would need to fetch the worst-case of N entries from each regulatory domain, and then re-rank/sort/paginate the aggregation of them.
This presents numerous significant problems: not just network latency on every single page load (which would be severe), but also bandwidth consumption, memory, and cpu wherever the aggregation/ranking are being performed. I don't think your option A is even feasible for a worldwide social network, with many users in countries where less powerful devices are prevalent. Even option B would have scary implications for the worldwide increase in network bandwidth consumption alone.
And that's not even considering the problem of how you migrate one of the largest data sets in human history from a non-geo-sharded topology to a geo-sharded one.
Worst-case, multiply all of this effort (and bandwidth impact) by every single multi-national social network, publishing platform, and user-generated content site/app/product. The GDPR-compliance efforts of 2017-2018, which were quite substantial at all of these companies, would look like peanuts in comparison.