You don't seem to want logistic regression at all. What you say is "I would like to maximize the difference between true positives and false positives." That is a fine objective function, but it is not logistic regression. Let's see what it is.
First, some notation. The dependent variable is going to be $Y_i$:
\begin{align}
Y_i &= \left\{ \begin{array}{l}
1 \qquad \textrm{Purchase $i$ was profitable}\\
0 \qquad \textrm{Purchase $i$ was un-profitable}
\end{array}
\right.
\end{align}
The independent variables (the stuff you use to try to predict whether you should buy) are going to be $X_i$ (a vector). The parameter you are trying to estimate is going to be $\beta$ (a vector). You will predict buy when $X_i\beta>0$. For observation $i$, you predict buy when $X_i\beta>0$ or when the indicator function $\mathbf{1}_{X_i\beta>0}=1$.
A true positive happens on observation $i$ when both $Y_i=1$ and $\mathbf{1}_{X_i\beta>0}=1$. A false positive on observation $i$ happens when $Y_i=0$ and $\mathbf{1}_{X_i\beta>0}=1$. You wish to find the $\beta$ which maximizes true positives minus false positives, or:
\begin{equation}
max_\beta \; \sum_{i=1}^N Y_i\cdot\mathbf{1}_{X_i\beta>0} - \sum_{i=1}^N (1-Y_i)\cdot\mathbf{1}_{X_i\beta>0}
\end{equation}
This is not an especially familiar objective function for estimating a discrete response model, but bear with me while I do a little algebra on the objective function:
\begin{align}
&\sum_{i=1}^N Y_i\cdot\mathbf{1}_{X_i\beta>0} - \sum_{i=1}^N (1-Y_i)\cdot\mathbf{1}_{X_i\beta>0}\\
= &\sum_{i=1}^N Y_i\cdot\mathbf{1}_{X_i\beta>0} - \sum_{i=1}^N \mathbf{1}_{X_i\beta>0}
+ \sum_{i=1}^N Y_i\cdot\mathbf{1}_{X_i\beta>0}\\
= &\sum_{i=1}^N Y_i\cdot\mathbf{1}_{X_i\beta>0} - \sum_{i=1}^N \mathbf{1}_{X_i\beta>0}
+ \sum_{i=1}^N Y_i\cdot\mathbf{1}_{X_i\beta>0} \\
& \qquad + \sum_{i=1}^N 1 - \sum_{i=1}^N 1 + \sum_{i=1}^N Y_i - \sum_{i=1}^N Y_i\\
= &\sum_{i=1}^N Y_i\cdot\mathbf{1}_{X_i\beta>0} + \sum_{i=1}^N (1-Y_i)(1-\mathbf{1}_{X_i\beta>0}) - \sum_{i=1}^N 1 + \sum_{i=1}^N Y_i \\
\end{align}
OK, now notice that the last two terms in that sum are not functions of $\beta$, so we can ignore them in the maximization. Finally, we have just shown that the problem you want to solve, "maximize the difference between true positives and false positives" is the same as this problem:
\begin{equation}
max_\beta \; \sum_{i=1}^N Y_i\cdot\mathbf{1}_{X_i\beta>0} + \sum_{i=1}^N (1-Y_i)(1-\mathbf{1}_{X_i\beta>0})
\end{equation}
Now, that estimator has a name! It is named the maximum score estimator. It is a very intuitive way to estimate the parameter of a discrete response model. The parameter is chosen so as to maximize the number of correct predictions. The first term is the number of true positives, and the second term is the number of true negatives.
This is a pretty good way to estimate a (binary) discrete response model. The estimator is consistent, for example. (Manski, 1985, J of Econometrics) There are some oddities to this estimator, though. First, it is not unique in small samples. Once you have found one $\beta$ which solves the maximization, then any other $\beta$ which makes the exact same predictions in your dataset will solve the maximization---so, infinitely many $\beta$s close to the one you found. Also, the estimator is not asymptotically normal, and it converges slower than typical maximum likelihood estimators---cube root $N$ instead of root $N$ convergence. (Kim and Pollard, 1990, Ann of Stat) Finally, you can't use bootstrapping to do inference on it. (Abrevaya & Huang, 2005, Econometrica) There are some papers using this estimator though---there is a fun one about predicting results in the NCAA basketball tournament by Caudill, International Journal of Forecasting, April 2003, v. 19, iss. 2, pp. 313-17.
An estimator that overcomes most of these problems is Horowitz's smoothed maximum score estimator (Horowitz, 1992, Econometrica and Horowitz, 2002, J of Econometrics). It gives a root-$N$ consistent, asymptotically normal, unique estimator which is amenable to bootstrapping. Horowitz provides example code to implement his estimator on his webpage.
Best Answer
Call your false positive rate p, the actual negative rate in your sample r, and n the number of trials without coming across a false positive. Clearly for your confidence interval, given you've observed no false positives so far, the lowest bound (and indeed the maximum likelihood estimate) is zero. For the higher bound, you can think "what is the value of p for which there is $\alpha$ probability of getting 0 failures out of my n trials? Higher values of p are not in your $1-\alpha%$ confidence interval.
The probability of a false positive for any one draw in your sample experiment is $p\times{r}$.
So solve $\alpha=(1-pr)^n$
and you get $p=\frac{1-e^{\frac{log(\alpha)}{n}}}{r}$
With your n=32000 and r=.5 (if I understand your question correctly) this suggests the upper bound of a 95% confidence interval for false positives is 0.0001872245.