store_in_memory_spend_updates_in_redis drained the in-memory queues
into local variables before the rpush pipeline. If rpush raised (cloud
Redis hiccup, timeout, connection blip), those already-drained
transactions were garbage-collected with the scheduler job, silently
losing all spend aggregated during that tick.
Wrap the rpush in try/except. On failure, re-enqueue the aggregated
transactions into their respective in-memory queues so the next
scheduler tick retries.
Add a unit test that seeds real queues, simulates an rpush failure,
and asserts the transactions land back in-memory.
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