Confused why we need to make non stationary time series stationary before applying Arima or Sarima

I understand before one can apply Arima or Sarima for a time series, one needs to make the non stationary time series into stationary.

But making it stationary also means removing the trends and seasonality. Then how can these techniques fully capture the time series' properties? Would they be more predictive if there are components in their model that capture the trend and seasonality?

Second question, Sarima has a seasonal component, is it still necessary to make a job stationary …

apply arima datascience making sarima seasonality series time series trends