What is the intuition of a GARCH model without fitting ARMA for the conditional mean?

I wanted to ask, as I've seen this used a couple of times before, about the logic of fitting a GARCH model in absence of estimating ARMA for a series that is clearly an ARMA process (Fitting a GARCH model on an intercept only mean model for data that is an ARMA process).

Would this at all offer anything valuable in terms of investigating conditional variance?

I am asking this as I am currently tasked with investigating conditional variance of a time series. After estimating an appropriate ARMA model, I see that squared residuals are uncorrelated and archlm test for ARCH effects is insignificant at all lags, therefore I cannot fit any GARCH model.

What are my avenues of investigating conditional variance of the series for which I cannot fit a GARCH model?