## Step 1: Define the symbolic representation of the variables
The variables are 'automatic alerts' and 'SOC operators', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $3.48x_1^2 + 9.91x_1x_2 + 4.33x_1$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $6x_1 + 5x_2 \leq 28$ (network integrity impact)
- $2x_1 + 10x_2 \leq 73$ (power consumption)
- $x_1^2 + x_2^2 \geq 13$ (total combined network integrity impact squared)
- $2x_1 + 10x_2 \geq 16$ (power consumption contribution)
- $10x_1 - 8x_2 \geq 0$ (relationship between automatic alerts and SOC operators)
- $6x_1 + 5x_2 \leq 14$ (total combined network integrity impact)
- $2x_1 + 10x_2 \leq 58$ (power consumption limit)
- $x_1, x_2$ are integers (whole number of automatic alerts and SOC operators)

## 4: Correct and Simplify Constraints
Upon review, notice that $6x_1 + 5x_2 \leq 28$ and $6x_1 + 5x_2 \leq 14$ can be simplified or considered as a single constraint $6x_1 + 5x_2 \leq 14$ since it's more restrictive. Also, $2x_1 + 10x_2 \leq 73$ and $2x_1 + 10x_2 \leq 58$ can be simplified to $2x_1 + 10x_2 \leq 58$ for the same reason.

## 5: Symbolic Representation
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'automatic alerts'), ('x2', 'SOC operators')],
'objective_function': '3.48*x1^2 + 9.91*x1*x2 + 4.33*x1',
'constraints': [
    '6*x1 + 5*x2 <= 14',
    '2*x1 + 10*x2 <= 58',
    'x1^2 + x2^2 >= 13',
    '2*x1 + 10*x2 >= 16',
    '10*x1 - 8*x2 >= 0',
    'x1 >= 0', 'x2 >= 0', 'x1 % 1 == 0', 'x2 % 1 == 0'
]
}
```

## 6: Gurobi Code
Now, let's implement this in Gurobi:
```python
import gurobi

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

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

    # Objective function
    model.setObjective(3.48*x1**2 + 9.91*x1*x2 + 4.33*x1, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(6*x1 + 5*x2 <= 14, name="network_integrity")
    model.addConstr(2*x1 + 10*x2 <= 58, name="power_consumption")
    model.addConstr(x1**2 + x2**2 >= 13, name="network_integrity_squared")
    model.addConstr(2*x1 + 10*x2 >= 16, name="power_consumption_contribution")
    model.addConstr(10*x1 - 8*x2 >= 0, name="alerts_vs_operators")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Automatic Alerts: {x1.varValue}")
        print(f"SOC Operators: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```