You can just use the history of Y or also your suggested causal. I have not seen “sample of sales” before as a causal, so I am hesitant to want to use that variable, but I am sure you know what you are doing.
Yes, you should consider the adjustment of outliers. Yes, you should allow for a constant. Yes, you should consider seasonal impacts.
The ACF/PACF doesn't show that the lag of 4 is important so autoregressive seasonality is weak. The data are short so this can be expected. Q4 is flat and then the last year Q4 is high which might due to the short data or a change in the behavior of Q4. Tough to tell.
A possible model (automatically developed using AUTOBOX), a piece of software I have helped develop is providing There are two seasonal dummies detected consistent 1st and 2nd qtr positive effects.
If one did not use the predictor then a very similar forecast is developed using this equation
It is interesting (at least to me !) that the two quarterly negative seasonal pulses (qtrs 3 and 4) are the “reflection” of the two quarterly seasonal pulses developed using the predictor series.
EDITED to respond to Nick's OLS MODEL:
If you take Y and divide it by X to get a new variable called Z and THEN run an OLS model restricting the intercept to be 0., you in fact will obtain . The residuals from this assumed model (as you have wisely said in previous posts it is always a good thing to bring the residuals to your "doctor" for a checkup) have a serious violation/malady at period 1 and clearly evident non-randomness. The whole idea is to avoid entertaining insufficient models and adequately capturing the signal. Clearly, the simple OLS model for Z ignores the very clear need for seasonal/quarterly dummies which are lost in translation when converting Y and X to Z.