## Step 1: Define the symbolic representation of the variables
The variables are 'bowls of cereal', 'bagged salads', 'bananas', 'sashimi' which can be represented symbolically as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $8.23x_0 + 6.31x_1 + 5.0x_2 + 1.13x_3$.

## Step 3: List all the constraints in symbolic notation
1. $3x_0 + 8x_1 + 7x_2 \geq 23$ (at least 23 grams of fiber from bowls of cereal, bagged salads, and bananas)
2. $3x_0 + 8x_1 + 15x_3 \geq 23$ (at least 23 grams of fiber from bowls of cereal, bagged salads, and sashimi)
3. $3x_0 + 8x_1 + 7x_2 \geq 21$ (at least 21 grams of fiber from bowls of cereal, bagged salads, and bananas)
4. $3x_0 + 8x_1 + 15x_3 \geq 21$ (at least 21 grams of fiber from bowls of cereal, bagged salads, and sashimi)
5. $2x_1 + 17x_3 \geq 22$ (total healthiness rating from bagged salads and sashimi)
6. $12x_0 + 17x_3 \geq 17$ (total healthiness rating from bowls of cereal and sashimi)
7. $12x_0 + 10x_2 \geq 34$ (total healthiness rating from bowls of cereal and bananas)
8. $12x_0 + 2x_1 \geq 14$ (total healthiness rating from bowls of cereal and bagged salads)
9. $12x_0 + 2x_1 + 10x_2 \geq 24$ (total healthiness rating from bowls of cereal, bagged salads, and bananas)
10. $12x_0 + 2x_1 + 17x_3 \geq 24$ (total healthiness rating from bowls of cereal, bagged salads, and sashimi)
11. $2x_1 + 10x_2 + 17x_3 \geq 24$ (total healthiness rating from bagged salads, bananas, and sashimi)
12. $12x_0 + 2x_1 + 10x_2 \geq 17$ (total healthiness rating from bowls of cereal, bagged salads, and bananas)
13. $12x_0 + 2x_1 + 17x_3 \geq 17$ (total healthiness rating from bowls of cereal, bagged salads, and sashimi)
14. $2x_1 + 10x_2 + 17x_3 \geq 17$ (total healthiness rating from bagged salads, bananas, and sashimi)
15. $12x_0 + 2x_1 + 10x_2 \geq 29$ (total healthiness rating from bowls of cereal, bagged salads, and bananas)
16. $12x_0 + 2x_1 + 17x_3 \geq 29$ (total healthiness rating from bowls of cereal, bagged salads, and sashimi)
17. $2x_1 + 10x_2 + 17x_3 \geq 29$ (total healthiness rating from bagged salads, bananas, and sashimi)
18. $-2x_0 + 10x_3 \geq 0$ (constraint on bowls of cereal and sashimi)
19. $8x_1 + 7x_2 \leq 121$ (at most 121 grams of fiber from bagged salads and bananas)
20. $3x_0 + 7x_2 \leq 122$ (at most 122 grams of fiber from bowls of cereal and bananas)
21. $3x_0 + 8x_1 + 7x_2 + 15x_3 \leq 122$ (at most 122 grams of fiber from all)
22. $12x_0 + 2x_1 \leq 121$ (total healthiness rating from bowls of cereal and bagged salads)
23. $12x_0 + 17x_3 \leq 61$ (total healthiness rating from bowls of cereal and sashimi)
24. $2x_1 + 10x_2 \leq 138$ (total healthiness rating from bagged salads and bananas)
25. $12x_0 + 2x_1 + 17x_3 \leq 101$ (total healthiness rating from bowls of cereal, bagged salads, and sashimi)
26. $12x_0 + 2x_1 + 10x_2 + 17x_3 \leq 101$ (total healthiness rating from all)

## Step 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'bowls of cereal'),
        ('x1', 'bagged salads'),
        ('x2', 'bananas'),
        ('x3', 'sashimi')
    ],
    'objective_function': '8.23*x0 + 6.31*x1 + 5.0*x2 + 1.13*x3',
    'constraints': [
        '3*x0 + 8*x1 + 7*x2 >= 23',
        '3*x0 + 8*x1 + 15*x3 >= 23',
        '3*x0 + 8*x1 + 7*x2 >= 21',
        '3*x0 + 8*x1 + 15*x3 >= 21',
        '2*x1 + 17*x3 >= 22',
        '12*x0 + 17*x3 >= 17',
        '12*x0 + 10*x2 >= 34',
        '12*x0 + 2*x1 >= 14',
        '12*x0 + 2*x1 + 10*x2 >= 24',
        '12*x0 + 2*x1 + 17*x3 >= 24',
        '2*x1 + 10*x2 + 17*x3 >= 24',
        '12*x0 + 2*x1 + 10*x2 >= 17',
        '12*x0 + 2*x1 + 17*x3 >= 17',
        '2*x1 + 10*x2 + 17*x3 >= 17',
        '12*x0 + 2*x1 + 10*x2 >= 29',
        '12*x0 + 2*x1 + 17*x3 >= 29',
        '2*x1 + 10*x2 + 17*x3 >= 29',
        '-2*x0 + 10*x3 >= 0',
        '8*x1 + 7*x2 <= 121',
        '3*x0 + 7*x2 <= 122',
        '3*x0 + 8*x1 + 7*x2 + 15*x3 <= 122',
        '12*x0 + 2*x1 <= 121',
        '12*x0 + 17*x3 <= 61',
        '2*x1 + 10*x2 <= 138',
        '12*x0 + 2*x1 + 17*x3 <= 101',
        '12*x0 + 2*x1 + 10*x2 + 17*x3 <= 101'
    ]
}
```

## Step 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="bowls_of_cereal", lb=0)
    x1 = model.addVar(name="bagged_salads", lb=0)
    x2 = model.addVar(name="bananas", lb=0)
    x3 = model.addVar(name="sashimi", lb=0)

    # Define the objective function
    model.setObjective(8.23*x0 + 6.31*x1 + 5.0*x2 + 1.13*x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3*x0 + 8*x1 + 7*x2 >= 23)
    model.addConstr(3*x0 + 8*x1 + 15*x3 >= 23)
    model.addConstr(3*x0 + 8*x1 + 7*x2 >= 21)
    model.addConstr(3*x0 + 8*x1 + 15*x3 >= 21)
    model.addConstr(2*x1 + 17*x3 >= 22)
    model.addConstr(12*x0 + 17*x3 >= 17)
    model.addConstr(12*x0 + 10*x2 >= 34)
    model.addConstr(12*x0 + 2*x1 >= 14)
    model.addConstr(12*x0 + 2*x1 + 10*x2 >= 24)
    model.addConstr(12*x0 + 2*x1 + 17*x3 >= 24)
    model.addConstr(2*x1 + 10*x2 + 17*x3 >= 24)
    model.addConstr(12*x0 + 2*x1 + 10*x2 >= 17)
    model.addConstr(12*x0 + 2*x1 + 17*x3 >= 17)
    model.addConstr(2*x1 + 10*x2 + 17*x3 >= 17)
    model.addConstr(12*x0 + 2*x1 + 10*x2 >= 29)
    model.addConstr(12*x0 + 2*x1 + 17*x3 >= 29)
    model.addConstr(2*x1 + 10*x2 + 17*x3 >= 29)
    model.addConstr(-2*x0 + 10*x3 >= 0)
    model.addConstr(8*x1 + 7*x2 <= 121)
    model.addConstr(3*x0 + 7*x2 <= 122)
    model.addConstr(3*x0 + 8*x1 + 7*x2 + 15*x3 <= 122)
    model.addConstr(12*x0 + 2*x1 <= 121)
    model.addConstr(12*x0 + 17*x3 <= 61)
    model.addConstr(2*x1 + 10*x2 <= 138)
    model.addConstr(12*x0 + 2*x1 + 17*x3 <= 101)
    model.addConstr(12*x0 + 2*x1 + 10*x2 + 17*x3 <= 101)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("bowls_of_cereal: ", x0.varValue)
        print("bagged_salads: ", x1.varValue)
        print("bananas: ", x2.varValue)
        print("sashimi: ", x3.varValue)
    else:
        print("The model is infeasible")

optimize_problem()
```