GLM with grouped/aggregated data in R

I would like to fit a GLM to the rate underlying a Poisson process, for data with variable exposure (period of measurement) - and the question is about aggregating/grouping the data before fitting or not. So the model is

$$\mu = \exp(\beta_0+\beta_1 X_1+\beta_2 X_2)$$
$$Y_i \sim Poisson(\mu_i t_i)$$

So $\mu_i$ is the rate, $t_i$ is the time over which the observations were recorded, and $Y_i$ is the Poissonian distributed number of counts measured in $t_i$. The exposure ($t_i$) should therefore, by my understanding, appear as both an offset in the GLM and a weight (longer observations get more weight). I code this up in R as the following:

#generate the data:
numsamples<-50000
x1<-sample(1:20,numsamples,replace=T)
x2<-sample(1:10,numsamples,replace=T)
t<-1/sample(1:10,numsamples,replace=T)  #exposure time

mu_rate<- exp(0.1+0.04*x1+0.025*x2)  #log linear rate
#generate the count data:
y<-rpois(numsamples,mu_rate*t)
#combine the data into a data frame
df <- data.frame(y=y,x1=x1,x2=x2,t=t)

#fit a glm:
offset=log(df$t),weights=df$t/max(df$t)) #aggregate data with identical variables - sum both y and t df_agg<-aggregate(cbind(y,t)~x1+x2,data=df,FUN=sum) #fit a glm to the aggregated data glm1<-glm(y~x1+x2,data=df_agg,family=poisson(link ="log"), offset=log(df_agg$t),weights=df_agg$t/max(df_agg$t))


Here I have fit to the raw data, and also aggregated data with identical values of $X_1$ and $X_2$ by summing the total count $Y$ and exposure $t$.

Now, my understanding is that aggregating the data should make no difference to the fit (provided that one offsets and weights appropriately by the newly aggregated exposure $t$) - see, for example, page 10 of http://data.princeton.edu/wws509/notes/c4.pdf . However, the two glm fits give different coefficients (e.g.: 0.1051 vs 0.1065 for the intercept). Admittedly, here, the difference is less than the standard error in the coefficients - however a) I would have expected no difference at all except for machine precision errors, and b) on more complicated data sets which I can't replicate here, the discrepancy is considerably larger than the standard error. Increasing the maximum iterations and decreasing the tolerence (epsilon) seemed to have no effect.

So I guess, my question boils down to a) is there something wrong with the way I have offset/weighted my data or done the aggregation? and b) is there something wrong with my expectation of obtaining the identical fit parameters with the aggregated data?