## Step 1: Define the symbolic representation of the variables
The variables are 'strips of bacon' and 'potatoes', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to maximize is $9x_1^2 + 3x_1x_2 + 7x_1 + 2x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
- $2x_1 + 4x_2 \geq 8$ (total combined healthiness rating)
- $6x_1 + 3x_2 \geq 21$ (total combined sourness index)
- $10x_1^2 + 7x_2^2 \geq 29$ (milligrams of iron)
- $10x_1^2 + x_2^2 \geq 20$ (cost constraint)
- $-2x_1^2 + 5x_2^2 \geq 0$ (relationship between $x_1$ and $x_2$)
- $2x_1^2 + 4x_2^2 \leq 41$ (total combined healthiness rating squared)
- $2x_1 + 4x_2 \leq 41$ (total combined healthiness rating)
- $6x_1 + 3x_2 \leq 41$ (total combined sourness index)
- $10x_1 + 7x_2 \leq 52$ (milligrams of iron)
- $10x_1 + x_2 \leq 26$ (cost constraint)

## 4: Convert the problem into a Gurobi code
We will use Gurobi to solve this optimization problem.

```python
import gurobi as gp

# Define the model
m = gp.Model()

# Define the variables
x1 = m.addVar(name="strips_of_bacon", lb=-gp.GRB.INFINITY)
x2 = m.addVar(name="potatoes", lb=-gp.GRB.INFINITY)

# Define the objective function
m.setObjective(9*x1**2 + 3*x1*x2 + 7*x1 + 2*x2, gp.GRB.MAXIMIZE)

# Define the constraints
m.addConstr(2*x1 + 4*x2 >= 8, name="healthiness_rating")
m.addConstr(6*x1 + 3*x2 >= 21, name="sourness_index")
m.addConstr(10*x1**2 + 7*x2**2 >= 29, name="milligrams_of_iron")
m.addConstr(10*x1**2 + x2**2 >= 20, name="cost_constraint_1")
m.addConstr(-2*x1**2 + 5*x2**2 >= 0, name="relationship_between_x1_x2")
m.addConstr(2*x1**2 + 4*x2**2 <= 41, name="healthiness_rating_squared")
m.addConstr(2*x1 + 4*x2 <= 41, name="healthiness_rating_linear")
m.addConstr(6*x1 + 3*x2 <= 41, name="sourness_index_linear")
m.addConstr(10*x1 + 7*x2 <= 52, name="milligrams_of_iron_linear")
m.addConstr(10*x1 + x2 <= 26, name="cost_constraint_2")

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Strips of bacon: ", x1.varValue)
    print("Potatoes: ", x2.varValue)
else:
    print("The model is infeasible")
```

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'strips of bacon'), ('x2', 'potatoes')],
    'objective_function': '9*x1^2 + 3*x1*x2 + 7*x1 + 2*x2',
    'constraints': [
        '2*x1 + 4*x2 >= 8',
        '6*x1 + 3*x2 >= 21',
        '10*x1^2 + 7*x2^2 >= 29',
        '10*x1^2 + x2^2 >= 20',
        '-2*x1^2 + 5*x2^2 >= 0',
        '2*x1^2 + 4*x2^2 <= 41',
        '2*x1 + 4*x2 <= 41',
        '6*x1 + 3*x2 <= 41',
        '10*x1 + 7*x2 <= 52',
        '10*x1 + x2 <= 26'
    ]
}
```