Seasonal differencing is relevant when the time series is seasonally integrated. Consider the simplest form of seasonal integration -- a SARIMA$(0,0,0)\times(0,1,0)_h$ model with a seasonal period $h$. The original time series under this model is made up of $h$ random walks that alternate every season. I.e. each season has its own random walk, and the random walks of the different seasons are unrelated.
Here is an example with $h=4$ (circles of different colours are used to distinguish between the seasons):
That may or may not be sensible in applications as you would not always expect the difference between two consecutive time points to have values that diverge from each other (which happens under seasonal integration).
A sign that a series is not seasonally integrated is significant PACF at seasonal lags after seasonal differencing. For a seasonally non-integrated series, taking seasonal differences does not solve a problem but rather creates one (the problem of overdifferencing). The presence of seasonal integration can be formally tested by OCSB or Canova-Hansen tests.
If the series is seasonally non-integrated, you may consider a SARIMA$(p,d,q)\times(P,0,Q)_h$ model or using dummy variables or Fourier terms.