I am far from an expert on X-13ARIMA-SEATS, but have worked with ARIMA models.
Consider this simple quarterly time series: 1, 2, 3, 14, 5, 6,7,18, 9, 10, 11, 22, ...
There is a linear trend of +1 in each quarter and a seasonal effect of +10 every Q4. What happens if you take a first seasonal difference? 5-1=4, 6-2=4, 7-3=4, 18-14=4, 9-5=4,... , which is stationary (both the trend and seasonality have been removed). In the ARIMA notation, this is the 1 in (0, 0, 0)(0, 1, 0). For your data, a first seasonal differencing was sufficient to achieve stationarity (required to identify and estimate the AR and MA patterns) and there are no AR or MA patterns in the differenced data.
The seasonality appears to be a percentage rather than an amount, which suggests a log transformation. Try seas() without transform.function = "none" and it will decide whether to use a log pre-transformation or not.