To address the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves identifying the variables, the objective function, and the constraints.

Given variables:
- Security Engineers: $x_0$
- Intrusion Analysts: $x_1$

Objective Function:
The goal is to minimize $2x_0 + 6x_1$.

Constraints:
1. Network latency impact due to security engineers: $4x_0$
2. Dollar cost of security engineers: $13x_0$
3. Network latency impact due to intrusion analysts: $23x_1$
4. Dollar cost of intrusion analysts: $13x_1$
5. Minimum combined network latency: $4x_0 + 23x_1 \geq 80$
6. Minimum dollar spend: $13x_0 + 13x_1 \geq 72$
7. Constraint on the difference between security engineers and intrusion analysts: $x_0 - x_1 \geq 0$
8. Maximum network latency: $4x_0 + 23x_1 \leq 134$
9. Maximum dollar spend: $13x_0 + 13x_1 \leq 190$
10. Integer constraints for both security engineers and intrusion analysts.

Symbolic Representation:
```json
{
  'sym_variables': [('x0', 'security engineers'), ('x1', 'intrusion analysts')],
  'objective_function': '2*x0 + 6*x1',
  'constraints': [
    '4*x0 + 23*x1 >= 80',
    '13*x0 + 13*x1 >= 72',
    'x0 - x1 >= 0',
    '4*x0 + 23*x1 <= 134',
    '13*x0 + 13*x1 <= 190'
  ]
}
```

Now, let's implement this problem in Gurobi Python:

```python
from gurobipy import *

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

# Add variables to the model
x0 = m.addVar(vtype=GRB.INTEGER, name="security_engineers")
x1 = m.addVar(vtype=GRB.INTEGER, name="intrusion_analysts")

# Set the objective function
m.setObjective(2*x0 + 6*x1, GRB.MINIMIZE)

# Add constraints to the model
m.addConstr(4*x0 + 23*x1 >= 80, "minimum_latency")
m.addConstr(13*x0 + 13*x1 >= 72, "minimum_cost")
m.addConstr(x0 - x1 >= 0, "engineers_vs_analysts")
m.addConstr(4*x0 + 23*x1 <= 134, "maximum_latency")
m.addConstr(13*x0 + 13*x1 <= 190, "maximum_cost")

# Optimize the model
m.optimize()

# Print the 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.")
```