Arbitrating differences in relative ordering across different observer clocks is what N-temporal databases are about. In databases we usually call the basic 2-temporal case “bitemporal”. The trivial 1-temporal case (which is a quasi-global clock) is what we call “time-series”.
The complexity is that N-temporality turns time into a true N-dimensional data type. These have different behavior than the N-dimensional spatial data types that everyone is familiar with, so you can’t use e.g. quadtrees as you would in the 2-spatial case and expect it to perform well.
There are no algorithms in literature for indexing N-temporal types at scale. It is a known open problem. That’s why we don’t do it in databases except at trivial scales where you can just brute-force the problem. (The theory problem is really interesting but once you start poking at it you quickly see why no one has made any progress on it. It hurts the brain just to think about it.)
Also even if not required makes reasoning about how systems work a hell lot easier. So for vast majority that doesn't need massive throughtputs sacrificing some speed for easier to understand consistency model is worthy tradeoff
Prety much all financial transactions are settled with a given date, not instantly. Go sell some stocks, it takes 2 days to actually settle. (May be hidden by your provider, but that how it works).
For that matter, the ultimate in BASE for financial transactions is the humble check.
That is a great example of "money out" that will only be settled at some time in the future.
There is a reason there is this notion of a "business day" and re-processing transactions that arrived out of order.