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

### Symbolic Representation

- **Variables:**
  - `x0`: automatic alerts (whole number)
  - `x1`: SOC operators (whole number)

- **Objective Function:**
  The goal is to maximize \(3.48 \times x0^2 + 9.91 \times x0 \times x1 + 4.33 \times x0\).

- **Constraints:**
  1. Network integrity impact of automatic alerts: \(6x0\)
  2. Power consumption of automatic alerts: \(2x0\) kWh
  3. Network integrity impact of SOC operators: \(5x1\)
  4. Power consumption added by SOC operators: \(10x1\) kWh
  5. Total combined network integrity impact: \((6x0)^2 + (5x1)^2 \geq 13\)
  6. Minimum power consumption: \(2x0 + 10x1 \geq 16\)
  7. Balance constraint: \(10x0 - 8x1 \geq 0\)
  8. Maximum total combined network integrity impact: \(6x0 + 5x1 \leq 14\)
  9. Maximum power consumption: \(2x0 + 10x1 \leq 58\)

Given this breakdown, the symbolic representation of the problem can be summarized as:

```json
{
    'sym_variables': [('x0', 'automatic alerts'), ('x1', 'SOC operators')],
    'objective_function': '3.48 * x0^2 + 9.91 * x0 * x1 + 4.33 * x0',
    'constraints': [
        '(6*x0)^2 + (5*x1)^2 >= 13',
        '2*x0 + 10*x1 >= 16',
        '10*x0 - 8*x1 >= 0',
        '6*x0 + 5*x1 <= 14',
        '2*x0 + 10*x1 <= 58'
    ]
}
```

### Gurobi Code

To solve this optimization problem using Gurobi, we'll write the Python code as follows:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="automatic_alerts")
x1 = m.addVar(vtype=GRB.INTEGER, name="SOC_operators")

# Objective function: Maximize 3.48*x0^2 + 9.91*x0*x1 + 4.33*x0
m.setObjective(3.48 * x0**2 + 9.91 * x0 * x1 + 4.33 * x0, GRB.MAXIMIZE)

# Constraints
m.addConstr((6*x0)**2 + (5*x1)**2 >= 13, name="total_network_integrity")
m.addConstr(2*x0 + 10*x1 >= 16, name="min_power_consumption")
m.addConstr(10*x0 - 8*x1 >= 0, name="balance_constraint")
m.addConstr(6*x0 + 5*x1 <= 14, name="max_total_network_integrity")
m.addConstr(2*x0 + 10*x1 <= 58, name="max_power_consumption")

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: x0 = {x0.X}, x1 = {x1.X}")
else:
    print("No optimal solution found")
```