A minimal Python app on my machine takes 6MB RAM.
There are use cases where 200mb of disk space matters, but for the overwhelming majority of applications, a 5% (say) increase in developer productivity would be well worth adding 200mb to the install.
And did you try spending a person-week or so of dev time trying to minimize the size of the Python version? That would be a much smaller one-off cost than the costs of migrating everything to Lua, and I could easily imagine you'd get it down by one or two orders of magnitude. (You could do the same for Lua too I'm sure, but if we're talking about say 5mb vs 200k then that's "who cares?" size for most customers).
From where do you get this impression that Python is the only high productivity language in the world? There are plenty of languages that can compete with Python in terms of productivity and a lot of them offer better abstractions for it. Lua is known for being one of the best escape hatches for scripting.
My opinion is that Python is more productive than Lua, but if you disagree by all means use Lua. But if you're talking about saving 200mb of disk space as though it makes the difference, then I think you're using the wrong criteria for choosing your language.
For example my local Python venv is about 130 MB. It symlinks in another 52 MB of standard Python libraries. And I know it won't run without some unknown amount of additional dependencies in C-libraries. If I wanted to send that to someone I'm starting at 200 MB. After compression that might be 40 MB or so. You're going to have to work to strip that down.
By comparison their download including Lua, code, and libraries is under 6 MB.
I have a minimal running Python application that was started recently and has done nothing. It takes close to 90 MB of RAM just to start.
Theirs is generally under 10 MB while it is running.
So at every step Python requires 5-10 times as much data. If you're trying to get other people to put you in their containers, this can matter a lot.