Here Graeme Walsh explains how to better look at the fit than just the coefficients:
ARIMA model interpretation
Here David J Harris describes differences in the model selection parameters AIC, BIC, etc.
AIC,BIC,CIC,DIC,EIC,FIC,GIC,HIC,IIC --- Can I use them interchangeably?
Here an earlier question regarding ARMA models
How do I choose which parameters to estimate in an ARMA model in python statsmodel?
Your question: *I would like to tell about all those numbers as much as possible. *
1) The first part is descriptive (like name and selected model which is straightforward) + some measures like the AIC, BIC, HQIC, which are measures that mix the likelihood with the number of parameters and data points. Various texts explain how they relate with selecting an ARMA model. One example is:
from this course http://halweb.uc3m.es/esp/Personal/personas/amalonso/esp/tsa.htm
page 50 of the 9th lecture explains that AIC tends to overfit whereas BIC is consistent because it penalizes axtra parameters more, yet AIC is better to model a process that is potentially of infinite order (I am not sure what that means but imagine an order that grows with the sample size)
2) The second part are the values of the model parameters plus their estimates.
Standard error relates to an estimate of the error of the predicted value (difference of the predicted value with the underlying "true" model value). This is a frequentists concept. If the experiment would (potentially) been done many times (with the same distribution of the residuals), how would the error be distributed (in simple words: how strong is the influence of the residual error terms on my estimate of the parameters, say if i do another test with similar distribution of the error terms then how much different parameters may I find just because of a different set of residual error terms?). The error is called 'standard error' because one expresses the estimate of the error in terms of the standard deviation, or the estimate of the second moment. This error may possibly calculated by some exact formula or less exact formula, one can also estimate it computationally it by simply creating a very large number of random data.
z This seems to be the standardized value of the coefficient values. It is $z=coef/std.error$ Just another way to express it and not really new information. $P(\vert z \vert)$ seems to me to be a t-test.
95% conf interval Is also no new information and seems to be the coefficient value +/- a certain number of the standard error. The percentage refers to the probabilit/frequency that the procedure would include the correct value in this interval.
The typical statistical interpretation stuff applies to these three concepts. But note the comment from Graeme Walsh in the linked reference that it is not so good to look at all these parameters on an individual basis.
3) The third part, with the complex numbers, relates to the roots of the characteristic equation of the model.
This is explained for instance here:
Using the characteristic equation several theoretic results can be expressed. See also in the answer by Graeme Walsh
ARIMA model interpretation
In your case you see for instance that the modulus is always >1 and therefore the process is stationary.