TLDD; No configuration needed. It is available to you, just need to code explicitely what you want to run in parallel.
JULIA_ACTIVE_THREADS is a configuration option for the Julia Kernel in Jupyter, not for the Python Kernel (the process that runs your notebook code).
Unless you run Jupyter inside a container, you can use out of the box all cores available in your system. If Jupyter is in a container or a virtual machine, it will use what you allocate and nothing more.
Just remember that by default you use 1 core when you run your Jupyter kernel.
When you run threading.active_count() and get 1, this means you are using one running thread on your code. Moden processors can use several threads for each available core. The bad news is that this is not a measure about how good you are using the cpu.
Python can act as an orchestrator for libraries that work in paraller behind the scenes (think numpy, pandas, tensorflow...).
If you want to code Python code that use more than 1 thread and/or 1 CPU, take a look at the multiprocess module.
The multipreocessing module is part of the standard library, and you can use it inside without trouble inside Jupyter. Probably you will find the Process and Pool methods useful (if you want to work with deep learning, there is a pytorch.multiprocessing module with the same interface but with support for working with GPUs in different threads).