The last three of those things might be valid issues, but since when are notebooks not just as subject as any other source code format to version control (I get that the difference between the UI and the on-site structure may make typical diff tools less-than-ideal, but VC itself is unaffected.)
I don't mean to sound harsh about other Data Scientists from a non software engineering background but the standard workflow is to fiddle around with a notebook until you can get a result. That's as far as it goes, no real robustness to it.
That's a pretty big generalisation but in organisations where they "home grow" their Data Science capability many of the online courses don't cover production level Data Science.
All the notebooks are in one place. Some are for important production jobs, other are for data exploration.
It's easy to make a little edit in a notebook and accidentally break production jobs.
Comparatively harder to make an edit in a git repo and do a deploy that'll break production jobs (e.g. if the JAR doesn't compile or the CI errors out cause the tests don't pass).
Notebook based production jobs get even more dangerous when NotebookA depends on NotebookB and so on.