## Step 1: Define the symbolic representation of the variables
The variables are 'oranges' and 'cornichons', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Define the resources/attributes for each variable
The resources/attributes are given as:
- $r_0$: sourness index
- $r_1$: tastiness rating
- $r_2$: umami index
- $r_3$: grams of carbohydrates

With their respective values for 'oranges' ($x_1$) and 'cornichons' ($x_2$):
- $r_0$: $x_1$ has a value of 1, $x_2$ has a value of 11
- $r_1$: $x_1$ has a value of 12, $x_2$ has a value of 3
- $r_2$: $x_1$ has a value of 8, $x_2$ has a value of 4
- $r_3$: $x_1$ has a value of 6, $x_2$ has a value of 9

## 3: Formulate the objective function
The objective function to maximize is $9x_1x_2 + 6x_2^2 + 5x_1 + 2x_2$.

## 4: Define the constraints
1. $x_1 + 11x_2 \geq 21$ (total combined sourness index)
2. $12^2x_1^2 + 3^2x_2^2 \geq 38$ (total combined tastiness rating squared)
3. $8^2x_1^2 + 4^2x_2^2 \geq 46$ (total combined umami index squared)
4. $6x_1 + 9x_2 \geq 21$ (total combined carbohydrates)
5. $-5x_1^2 + 7x_2^2 \geq 0$
6. $(x_1 + 11x_2)^2 \leq 36$ (total combined sourness index squared)
7. $12x_1 + 3x_2 \leq 57$ (total combined tastiness rating)
8. $8x_1 + 4x_2 \leq 144$ (total combined umami index)
9. $6x_1 + 9x_2 \leq 74$ (total combined carbohydrates)
10. $x_1$ is an integer
11. $x_2$ is an integer

## 5: Symbolic Representation
```json
{
    'sym_variables': [('x1', 'oranges'), ('x2', 'cornichons')],
    'objective_function': '9*x1*x2 + 6*x2^2 + 5*x1 + 2*x2',
    'constraints': [
        'x1 + 11*x2 >= 21',
        '12^2*x1^2 + 3^2*x2^2 >= 38',
        '8^2*x1^2 + 4^2*x2^2 >= 46',
        '6*x1 + 9*x2 >= 21',
        '-5*x1^2 + 7*x2^2 >= 0',
        '(x1 + 11*x2)^2 <= 36',
        '12*x1 + 3*x2 <= 57',
        '8*x1 + 4*x2 <= 144',
        '6*x1 + 9*x2 <= 74',
        'x1 >= 0 and x1 is an integer',
        'x2 >= 0 and x2 is an integer'
    ]
}
```

## 6: Gurobi Code
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="oranges", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="cornichons", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(9*x1*x2 + 6*x2**2 + 5*x1 + 2*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + 11*x2 >= 21)
    model.addConstr(12**2*x1**2 + 3**2*x2**2 >= 38)
    model.addConstr(8**2*x1**2 + 4**2*x2**2 >= 46)
    model.addConstr(6*x1 + 9*x2 >= 21)
    model.addConstr(-5*x1**2 + 7*x2**2 >= 0)
    model.addConstr((x1 + 11*x2)**2 <= 36)
    model.addConstr(12*x1 + 3*x2 <= 57)
    model.addConstr(8*x1 + 4*x2 <= 144)
    model.addConstr(6*x1 + 9*x2 <= 74)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("oranges: ", x1.varValue)
        print("cornichons: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```