Using an optimization library seems like the completely wrong approach to solving this interview question. Was that intentional?
As far as comparing code complexity of Julia to Python is concerned, I would say that when you use JuMP in Julia, you should use Pyomo/CasADi/PuLP/... in Python. That is not to say that I don't find JuMP to be a more appealing framework overall. It has wide support for all kinds of solvers, and some Julia/JuMP authors even wrote fairly good MIQCP/MICP solvers on top of commercial/open-source MILP/SOCP solvers.
Why do you say that? Perhaps if the knapsack problem is provably non-polynomial, but being able to distinguish those cases is a rather important skill.
For example, the problem you specified can more easily be solved directly: https://repl.it/repls/RotatingObeseBusinesses
With respect to the interview question context, if I had a candidate that implemented a solver, I would be inclined to say they completely overengineered the solution for a less optimal answer. There are a bunch of assumptions that are required for a solver to be optimal and unless the candidate can enumerate all of them and argue why the problem meets those constraints, the solver is not 100% guaranteed to be correct, whereas a direct solution is.
Not to mention, as you noted, edge cases, and the complete cryptic-ness of the code.