## Step 1: Define the symbolic representation of the problem
The problem involves maximizing the objective function: $2 \cdot oranges + 9 \cdot fruit\ salads + 9 \cdot bowls\ of\ cereal$. The variables are $oranges = x_0$, $fruit\ salads = x_1$, and $bowls\ of\ cereal = x_2$. The resources/attributes are given as $r_0$ for grams of carbohydrates, $r_1$ for milligrams of iron, $r_2$ for umami index, and $r_3$ for milligrams of calcium.

## Step 2: Convert the problem into a symbolic representation
The symbolic variables are: 
- $x_0$ for oranges
- $x_1$ for fruit salads
- $x_2$ for bowls of cereal

The objective function is: $2x_0 + 9x_1 + 9x_2$

## 3: List all constraints in symbolic notation
Constraints:
1. $4x_0 + x_1 + 4x_2 \geq 26$ (carbohydrates from oranges and bowls of cereal)
2. $x_0 + 2x_2 \geq 13$ (iron from oranges and bowls of cereal)
3. $x_0 + 8x_1 + 2x_2 \geq 18$ (total iron)
4. $7x_1 + x_2 \geq 19$ (umami index from fruit salads and bowls of cereal)
5. $8x_0 + x_2 \geq 11$ (umami index from oranges and bowls of cereal)
6. $4x_0 + x_1 \leq 54$ (carbohydrates from oranges and fruit salads)
7. $4x_0 + 4x_2 \leq 58$ (carbohydrates from oranges and bowls of cereal)
8. $4x_0 + x_1 + 4x_2 \leq 58$ (total carbohydrates)
9. $x_0 + 8x_1 \leq 62$ (iron from oranges and fruit salads)
10. $x_0 + 2x_2 \leq 49$ (iron from oranges and bowls of cereal)
11. $x_0 + 8x_1 + 2x_2 \leq 49$ (total iron)
12. $8x_0 + 7x_1 \leq 60$ (umami index from oranges and fruit salads)
13. $8x_0 + x_2 \leq 34$ (umami index from oranges and bowls of cereal)
14. $7x_1 + x_2 \leq 39$ (umami index from fruit salads and bowls of cereal)
15. $8x_0 + 7x_1 + x_2 \leq 39$ (total umami index)
16. $2x_0 + 8x_2 \leq 80$ (calcium from oranges and bowls of cereal)
17. $2x_0 + 6x_1 + 8x_2 \leq 80$ (total calcium)

## 4: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [('x0', 'oranges'), ('x1', 'fruit salads'), ('x2', 'bowls of cereal')],
    'objective_function': '2*x0 + 9*x1 + 9*x2',
    'constraints': [
        '4*x0 + x1 + 4*x2 >= 26',
        'x0 + 2*x2 >= 13',
        'x0 + 8*x1 + 2*x2 >= 18',
        '7*x1 + x2 >= 19',
        '8*x0 + x2 >= 11',
        '4*x0 + x1 <= 54',
        '4*x0 + 4*x2 <= 58',
        '4*x0 + x1 + 4*x2 <= 58',
        'x0 + 8*x1 <= 62',
        'x0 + 2*x2 <= 49',
        'x0 + 8*x1 + 2*x2 <= 49',
        '8*x0 + 7*x1 <= 60',
        '8*x0 + x2 <= 34',
        '7*x1 + x2 <= 39',
        '8*x0 + 7*x1 + x2 <= 39',
        '2*x0 + 8*x2 <= 80',
        '2*x0 + 6*x1 + 8*x2 <= 80'
    ]
}
```

## 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    oranges = model.addVar(name="oranges", lb=0)
    fruit_salads = model.addVar(name="fruit_salads", lb=0)
    bowls_of_cereal = model.addVar(name="bowls_of_cereal", lb=0)

    # Define the objective function
    model.setObjective(2 * oranges + 9 * fruit_salads + 9 * bowls_of_cereal, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(4 * oranges + fruit_salads + 4 * bowls_of_cereal >= 26)
    model.addConstr(oranges + 2 * bowls_of_cereal >= 13)
    model.addConstr(oranges + 8 * fruit_salads + 2 * bowls_of_cereal >= 18)
    model.addConstr(7 * fruit_salads + bowls_of_cereal >= 19)
    model.addConstr(8 * oranges + bowls_of_cereal >= 11)
    model.addConstr(4 * oranges + fruit_salads <= 54)
    model.addConstr(4 * oranges + 4 * bowls_of_cereal <= 58)
    model.addConstr(4 * oranges + fruit_salads + 4 * bowls_of_cereal <= 58)
    model.addConstr(oranges + 8 * fruit_salads <= 62)
    model.addConstr(oranges + 2 * bowls_of_cereal <= 49)
    model.addConstr(oranges + 8 * fruit_salads + 2 * bowls_of_cereal <= 49)
    model.addConstr(8 * oranges + 7 * fruit_salads <= 60)
    model.addConstr(8 * oranges + bowls_of_cereal <= 34)
    model.addConstr(7 * fruit_salads + bowls_of_cereal <= 39)
    model.addConstr(8 * oranges + 7 * fruit_salads + bowls_of_cereal <= 39)
    model.addConstr(2 * oranges + 8 * bowls_of_cereal <= 80)
    model.addConstr(2 * oranges + 6 * fruit_salads + 8 * bowls_of_cereal <= 80)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("Oranges: ", oranges.varValue)
        print("Fruit Salads: ", fruit_salads.varValue)
        print("Bowls of Cereal: ", bowls_of_cereal.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```