Some people really just want Python+extras and don't want to worry about getting a fortran compiler. Conda is great for this.
Other people really don't want a big meta-install system that bundles a bunch of dependencies. Maybe they're on Linux, they target a single platform, or they want to use a package not in conda that works with a more recent version of the dependencies that conda bundles.
For some people Python is really more of an end user application. They don't want to develop and distribute and deploy software, they want to write scripts and analyze and transform data. They expect the script a colleague emailed them to just work and expect their colleague to be able to open and run their Jupyter notebook as easily as they can open and run an Excel document. For those people Conda comes much closer to solving their problems.
Conda will package and version things like compilers and C libraries etc in a sane way. I can 'conda install' a package and start using it, but if i 'pip install' the same package I'm still left having to hunt down and install a bunch of additional dependencies. For example 'conda install cython' gives me a fully working and consistent cython on all platforms in a way that 'pip install cython' won't.
There are still several computational science and data science packages where 'pip install X' on windows simply doesn't work (and 'conda install X' does) due to missing dependencies and other weirdness.
Conda can version and manage R as well as Python if you work on projects that use both then you only need one tool.
Conda can also install dev tools like Spyder, VSCode or Jupyter(labs) making it easier to offer a single point of entry for an entire dev environment.
But basically the big one is simply that Conda will install a lot more numeric packages with a lot fewer headaches on win, linux and mac than pip. Although in pip's defense it has gotten a lot better over the past 2-3 years. And I do agree that the dependency resolver can take a ridiculously long time in some situations.