You can measure your sensitivity in an arbitrary norm. The exponential mechanism, that samples from the distribution proportional to $\exp(-\epsilon |z-f(x)|_p / 2)$ will give pure DP. This is more general, and the K-norm mechanism will allow you handle sensitivity measured in norms other than some p-norm. For pure DP, these p-norm mechanisms are optimal. In particular, when sensitivity is measured in the $\ell_2$ norm, the optimal mechanism adds noise from the Gamma distribution.
How then does the Gaussian noise mechanism do better? It gives you $(\epsilon, \delta)$-DP instead of pure DP. It is natural to ask what the optimal approximate DP mechanism is for other sensitivity measures. It turns out that for approximate DP, the right Gaussian mechanism is always optimal. You just convert your $\ell_p$ sensitivity bound into an $\ell_2$ sensitivity bound (by finding the smallest $\ell_2$ ball that contains your $\ell_p$ ball) and add Gaussian noise based on that. Here again, there are analogous results for sensitivity being measured in non-p-norms.
Some caveats are in order. Optimal above is for your error being measured in the Euclidean distance. Some of the results are optimal only up to logarithmic factors. When you care about error in other norms (say $\ell_\infty$), there can be other more interesting mechanisms.
The upper bounds for $\ell_p$ balls are straightforward. The lower bounds are in
https://arxiv.org/abs/0907.3754, and https://arxiv.org/abs/1212.0297. Simpler lower bounds for CDP/RDP are in https://arxiv.org/abs/1911.08339.