## Step 1: Define the symbolic representation of the variables
The variables are 'security engineers' and 'intrusion analysts', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $2x_1 + 6x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
1. $4x_1 + 23x_2 \geq 80$ (combined network latency contribution)
2. $13x_1 + 13x_2 \geq 72$ (minimum cost)
3. $x_1 - x_2 \geq 0$ (relationship between security engineers and intrusion analysts)
4. $4x_1 + 23x_2 \leq 134$ (maximum network latency contribution)
5. $13x_1 + 13x_2 \leq 198$ (maximum cost)
6. $x_1 \geq 0$ and $x_1$ is an integer (non-negative integer number of security engineers)
7. $x_2 \geq 0$ and $x_2$ is an integer (non-negative integer number of intrusion analysts)

## 4: Adjust constraints for accurate representation
Given that $x_1$ and $x_2$ must be integers, we keep the constraints as is, noting that Gurobi can handle integer variables directly.

## 5: Symbolic Representation
```json
{
'sym_variables': [('x1', 'security engineers'), ('x2', 'intrusion analysts')],
'objective_function': '2*x1 + 6*x2',
'constraints': [
    '4*x1 + 23*x2 >= 80',
    '13*x1 + 13*x2 >= 72',
    'x1 - x2 >= 0',
    '4*x1 + 23*x2 <= 134',
    '13*x1 + 13*x2 <= 198',
    'x1 >= 0', 'x2 >= 0'
]
}
```

## 6: Gurobi Code
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="security_engineers", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="intrusion_analysts", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(2*x1 + 6*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(4*x1 + 23*x2 >= 80, name="network_latency_min")
    model.addConstr(13*x1 + 13*x2 >= 72, name="cost_min")
    model.addConstr(x1 - x2 >= 0, name="security_engineers_vs_intrusion_analysts")
    model.addConstr(4*x1 + 23*x2 <= 134, name="network_latency_max")
    model.addConstr(13*x1 + 13*x2 <= 198, name="cost_max")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Security Engineers: {x1.varValue}")
        print(f"Intrusion Analysts: {x2.varValue}")
        print(f"Objective Function Value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```