If I do the seasonality test it gives me less than 0.04 in the P value, but it is not capable of forecasting and the seasonality is lost more if I add more years

An ARIMA(0,0,1) is *not* nonstationary. And forecasts are constant after one period out. And there is no seasonality estimated.

Maybe you want a different model?

I only want to forecast with the rain data I have. I have performed power functions but it does not change the result; If there is another library where I can forecast non-stationary data, it would help me

I'm not sure what you've done. The picture shows an ARIMA(0,0,1), which is stationary. A nonstationary model would have the second argument (typically) be a 1.

I am using auto.arima to study which is the best model, and from there I start to do it

myarima<- auto.arima(serie_lluvia,trace =TRUE,stepwise = FALSE, approximation = FALSE, max.d = 0)

Setting max.d=0 only allows for stationary series. Is that what you want?

what I want is a model where I can graph the forecast without giving me a linear value

Well, you can eliminate the max.d=0, although my guess is that won't matter.

You can use `arima`

rather than `auto.arima`

and choose higher-order parameters for the first argument.