1) I do not know for sure why you get many NAs in there, but most probably your window is too large. Decompose works by sliding window smoothing - it uses some radius $r$ (usually around 2 times smaller than the seasonality unless you changed it) to go through all values of time series, and sums up $r$ values to the left and right of each time series observation. The first and last $r$ values in the time series will be NA, since the window cannot be estimated ($r$ goes beyond the bounds). In your case you should have around $12 \times 7$ values as NA from each side of time series. However, you still have the necessary component in your $figure$ variable.
2) Take you initial time series, and simply subtract the "figure" from it repeatedly - simply move this window along the time series. This will give you a deseasonalized component (trend + cycle + error). However, if you have multiple seasonalities, you simply either repeat the procedure, but a better way is to use SARIMA - seasonal ARIMA. It will also difference your second season on demand by specifying the large $D$.
3) After you get your SARIMA forecast, simply add your seasonal figure (the one that you subtracted) onto it, step-by-step iteratively again. In case you had 2 seasonal removals and ARIMA, use ARIMA forecast, and first add second seasonal figure iteratively, then add the first seasonal figure iteratively.
However you should try modelling seasonality differently - such a large seasonal figure ($24 \times 7$) will be a bad estimate most probably. Try looking into deseasonalization with smaller period up to 20, or use Fourier series, which is very simple. Check on Hyndman's website: https://robjhyndman.com/publications/complex-seasonality