To solve this optimization problem, we need to define the decision variables, the objective function, and the constraints. The decision variables are the number of security engineers (`x0`) and the number of intrusion analysts (`x1`). 

The objective function aims to minimize `2*x0 + 6*x1`, representing the total cost or value associated with hiring these professionals.

There are several constraints based on network latency, dollar costs, and integer requirements for both types of professionals. We will use Gurobi's Python interface to model and solve this problem.

Here is how we can formulate the problem in Gurobi:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define decision variables
x0 = m.addVar(vtype=GRB.INTEGER, name="security_engineers")
x1 = m.addVar(vtype=GRB.INTEGER, name="intrusion_analysts")

# Objective function: Minimize 2*x0 + 6*x1
m.setObjective(2*x0 + 6*x1, GRB.MINIMIZE)

# Constraints:
# 1. security engineers each cause 4 milliseconds of additional network latency.
#    intrusion analysts create 23 milliseconds in additional network latency each.
#    Combined latency should be at least 80 ms and no more than 134 ms.
m.addConstr(4*x0 + 23*x1 >= 80, "min_latency")
m.addConstr(4*x0 + 23*x1 <= 134, "max_latency")

# 2. security engineers cost 13 dollars each. intrusion analysts are $13 each.
#    Spend at least $72 on both and no more than $190.
m.addConstr(13*x0 + 13*x1 >= 72, "min_cost")
m.addConstr(13*x0 + 13*x1 <= 190, "max_cost")

# 3. 1 times the number of security engineers, plus -1 times the number of intrusion analysts should be greater than or equal to zero.
m.addConstr(x0 - x1 >= 0, "security_vs_intrusion")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Security Engineers: {x0.x}")
    print(f"Intrusion Analysts: {x1.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```

This code defines the model, adds variables and constraints based on the problem description, optimizes the model, and then prints out the solution if one is found.