A few things here:
The customers were split into 2 groups - 1st group - customers with an id ending with 0 - control 2nd group - customers with an id ending with 1-9 - treatment
This is not proper randomization. Probably fine for a one-experiment, but if you use this scheme in repeated experiments, then you will be testing systematic differences between those with an ID ending in 0, and everyone else. Its best to literally randomize using a pRNG or something. Additionally, by only putting users with an ID ending in 0 in the control, you've hurt yourself with respect to power. The most efficient designs are 50/50 designs. Aside from that...
When comparing the difference between the groups I can see 10% uplift in terms of total spent (after multiplying by 9 to overcome the size difference).
I understand why you did this, and I think its a bad idea. The total amount spent is simply the product of the sample mean and the sample size. Since we know a lot about the sample mean, I think its better to analyze that.
The uplift is already exist in the groups even before the campaigns. This uplift is sourced to less then 0.1% of the customers - some of the customers spent a lot.
This is unsurprising to me, many types of comapnies have so called "whales": users who spend thousands of dollars and drive the majority of the revenue.
What I might suggest is that you use log spend instead. This can reign in the spend of whales so that OLS is other regression methods are more tenable. Then, you can use something like a difference in difference as seanv507 notes. However, if there are customers with spend=0 then this won't work for obvious reasons (and doing log 1 + spend doesn't give you valid estimates of the ATE either .
Does it make sense to compare the total spent in order to evaluate the effectiveness of the marketing campaigns? if not, why?
As I mentioned above, "No" because we know more about the properties of the mean and the mean is a rescaled total spend.