Does it matter if there's simultaneous determination between two dependent variables? For example, if I have an equation
$$
y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + u
$$

Where $x_1$ and $x_2$ are simultaneously determined, say

$$
x_1 = \gamma_0 + \gamma_1 x_2 + \gamma_2 z_1 + e
$$

$$
x_2 = \delta_0 + \delta_1 x_1 + \delta_2 z_2 + v
$$

Should I estimate using 2SLS or is this effect already contained when adding both variables to the main regression?

The concrete example I was thinking was for tax collection and drug cartels in Mexico:

$$
collection = \beta_0 + \beta_1 cartel_{memebers}+ \beta_2 GDP{per \  capita} + u
$$

Where the presence of drug cartels negatively affects GDP but cartels choose to settle on richer towns.
In your concrete example, I think 2SLS is something worth considering because the error term u (of $y$, i.e. of tax collection) is plausible in theory to be affected by presence of cartels in ways that make it more unreliable in reporting (e.g. via increased corruption). Of course you can/should test this endogeneity.