## Step 1: Define the symbolic representation of the variables
The variables are 'intrusion analysts' and 'Mbps bandwidth allocated to monitoring', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $9.77x_1 + 5.87x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
1. $6x_1 + 9x_2 \geq 9$ (at minimum, spend at least $9),
2. $x_1 + 6x_2 \geq 55$ (network latency must be at least 55 ms),
3. $-2x_1 + 6x_2 \geq 0$ (linear constraint),
4. $6x_1 + 9x_2 \leq 19$ (spend no more than $19),
5. $x_1 + 6x_2 \leq 76$ (network latency at most 76 ms),
6. $x_1$ is an integer (intrusion analysts must be a nonfractional number),
7. $x_2$ is an integer (Mbps bandwidth allocated must not be a fractional number).

## 4: Resource attributes
Given resources/attributes:
- $r_0$: {'description': 'dollar cost', 'upper_bound': 33, 'x0': 6, 'x1': 9},
- $r_1$: {'description': 'network latency impact', 'upper_bound': 128, 'x0': 1, 'x1': 6}.

## 5: Formulate the problem in Gurobi
To solve this problem using Gurobi, we need to formulate it as a mixed-integer linear programming (MILP) problem.

## 6: Write the Gurobi code
```python
import gurobi

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

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

    # Objective function: maximize 9.77*x1 + 5.87*x2
    model.setObjective(9.77*x1 + 5.87*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(6*x1 + 9*x2 >= 9, name="min_spend")
    model.addConstr(x1 + 6*x2 >= 55, name="min_latency")
    model.addConstr(-2*x1 + 6*x2 >= 0, name="linear_constraint")
    model.addConstr(6*x1 + 9*x2 <= 19, name="max_spend")
    model.addConstr(x1 + 6*x2 <= 76, name="max_latency")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Intrusion analysts: {x1.varValue}")
        print(f"Mbps bandwidth allocated: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 7: Symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'intrusion analysts'), ('x2', 'Mbps bandwidth allocated to monitoring')],
    'objective_function': '9.77*x1 + 5.87*x2',
    'constraints': [
        '6*x1 + 9*x2 >= 9',
        'x1 + 6*x2 >= 55',
        '-2*x1 + 6*x2 >= 0',
        '6*x1 + 9*x2 <= 19',
        'x1 + 6*x2 <= 76',
        'x1 is an integer',
        'x2 is an integer'
    ]
}
```