To tackle this problem, we first need to translate the given natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the provided information.

### Symbolic Representation:

- **Variables:**
  - Let $x_1$ represent the number of intrusion analysts.
  - Let $x_2$ represent the quantity of Mbps bandwidth allocated to monitoring.

- **Objective Function:**
  The goal is to maximize $9.77x_1 + 5.87x_2$.

- **Constraints:**
  1. Dollar cost constraint: $6x_1 + 9x_2 \leq 19$
  2. Minimum dollar cost: $6x_1 + 9x_2 \geq 9$
  3. Network latency constraint (lower bound): $x_1 + 6x_2 \geq 55$
  4. Network latency constraint (upper bound): $x_1 + 6x_2 \leq 76$
  5. Additional constraint: $-2x_1 + 6x_2 \geq 0$
  6. Non-negativity and integer constraints for both variables: $x_1, x_2 \geq 0$ and $x_1, x_2 \in \mathbb{Z}$

Given the above symbolic representation, we can express the problem in a structured format as requested:

```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 <= 19',
    '6*x1 + 9*x2 >= 9',
    'x1 + 6*x2 >= 55',
    'x1 + 6*x2 <= 76',
    '-2*x1 + 6*x2 >= 0'
  ]
}
```

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

```python
from gurobipy import *

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

# Add variables to the model
x1 = m.addVar(vtype=GRB.INTEGER, name="intrusion_analysts")
x2 = m.addVar(vtype=GRB.INTEGER, name="Mbps_bandwidth_allocated_to_monitoring")

# Set the objective function
m.setObjective(9.77*x1 + 5.87*x2, GRB.MAXIMIZE)

# Add constraints to the model
m.addConstr(6*x1 + 9*x2 <= 19, "dollar_cost_constraint")
m.addConstr(6*x1 + 9*x2 >= 9, "minimum_dollar_cost")
m.addConstr(x1 + 6*x2 >= 55, "network_latency_lower_bound")
m.addConstr(x1 + 6*x2 <= 76, "network_latency_upper_bound")
m.addConstr(-2*x1 + 6*x2 >= 0, "additional_constraint")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Intrusion Analysts: {x1.x}")
    print(f"Mbps Bandwidth Allocated to Monitoring: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```