## Step 1: Define the optimization problem
The problem is to maximize the objective function: $8.23x_0 + 6.31x_1 + 5.0x_2 + 1.13x_3$, where $x_0$ represents the number of bowls of cereal, $x_1$ represents the number of bagged salads, $x_2$ represents the number of bananas, and $x_3$ represents the number of sashimi.

## Step 2: List all the constraints
The constraints are as follows:
- $3x_0 + 8x_1 + 7x_2 \geq 23$ (at least 23 grams of fiber from bowls of cereal, bagged salads, and bananas)
- $3x_0 + 8x_1 + 15x_3 \geq 23$ (at least 23 grams of fiber from bowls of cereal, bagged salads, and sashimi)
- $3x_0 + 8x_1 + 7x_2 \geq 21$ (at least 21 grams of fiber from bowls of cereal, bagged salads, and bananas)
- $3x_0 + 8x_1 + 15x_3 \geq 21$ (at least 21 grams of fiber from bowls of cereal, bagged salads, and sashimi)
- $2x_1 + 17x_3 \geq 22$ (total healthiness rating from bagged salads and sashimi)
- $12x_0 + 17x_3 \geq 17$ (total healthiness rating from bowls of cereal and sashimi)
- $12x_0 + 10x_2 \geq 34$ (total healthiness rating from bowls of cereal and bananas)
- $12x_0 + 2x_1 \geq 14$ (total healthiness rating from bowls of cereal and bagged salads)
- $12x_0 + 2x_1 + 10x_2 \geq 24$ (total healthiness rating from bowls of cereal, bagged salads, and bananas)
- $12x_0 + 2x_1 + 17x_3 \geq 24$ (total healthiness rating from bowls of cereal, bagged salads, and sashimi)
- $2x_1 + 10x_2 + 17x_3 \geq 24$ (total healthiness rating from bagged salads, bananas, and sashimi)
- $12x_0 + 2x_1 + 10x_2 \geq 17$ (total healthiness rating from bowls of cereal, bagged salads, and bananas)
- $12x_0 + 2x_1 + 17x_3 \geq 17$ (total healthiness rating from bowls of cereal, bagged salads, and sashimi)
- $2x_1 + 10x_2 + 17x_3 \geq 17$ (total healthiness rating from bagged salads, bananas, and sashimi)
- $12x_0 + 2x_1 + 10x_2 \geq 29$ (total healthiness rating from bowls of cereal, bagged salads, and bananas)
- $12x_0 + 2x_1 + 17x_3 \geq 29$ (total healthiness rating from bowls of cereal, bagged salads, and sashimi)
- $2x_1 + 10x_2 + 17x_3 \geq 29$ (total healthiness rating from bagged salads, bananas, and sashimi)
- $-2x_0 + 10x_3 \geq 0$ (constraint on bowls of cereal and sashimi)
- $8x_1 + 7x_2 \leq 121$ (at most 121 grams of fiber from bagged salads and bananas)
- $3x_0 + 7x_2 \leq 122$ (at most 122 grams of fiber from bowls of cereal and bananas)
- $3x_0 + 8x_1 + 7x_2 + 15x_3 \leq 122$ (at most 122 grams of fiber from all)
- $12x_0 + 2x_1 \leq 121$ (total healthiness rating from bowls of cereal and bagged salads)
- $12x_0 + 17x_3 \leq 61$ (total healthiness rating from bowls of cereal and sashimi)
- $2x_1 + 10x_2 \leq 138$ (total healthiness rating from bagged salads and bananas)
- $12x_0 + 2x_1 + 17x_3 \leq 101$ (total healthiness rating from bowls of cereal, bagged salads, and sashimi)
- $12x_0 + 2x_1 + 10x_2 + 17x_3 \leq 101$ (total healthiness rating from all)

## 3: Define the bounds for the variables
The variables $x_0, x_1, x_2, x_3$ can take any non-negative real values.

## 4: 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(lb=0, name="bowls of cereal")
    x1 = model.addVar(lb=0, name="bagged salads")
    x2 = model.addVar(lb=0, name="bananas")
    x3 = model.addVar(lb=0, name="sashimi")

    # 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, name="fiber_from_cereal_salads_bananas")
    model.addConstr(3*x0 + 8*x1 + 15*x3 >= 23, name="fiber_from_cereal_salads_sashimi")
    model.addConstr(3*x0 + 8*x1 + 7*x2 >= 21, name="fiber_from_cereal_salads_bananas_2")
    model.addConstr(3*x0 + 8*x1 + 15*x3 >= 21, name="fiber_from_cereal_salads_sashimi_2")
    model.addConstr(2*x1 + 17*x3 >= 22, name="healthiness_from_salads_sashimi")
    model.addConstr(12*x0 + 17*x3 >= 17, name="healthiness_from_cereal_sashimi")
    model.addConstr(12*x0 + 10*x2 >= 34, name="healthiness_from_cereal_bananas")
    model.addConstr(12*x0 + 2*x1 >= 14, name="healthiness_from_cereal_salads")
    model.addConstr(12*x0 + 2*x1 + 10*x2 >= 24, name="healthiness_from_cereal_salads_bananas")
    model.addConstr(12*x0 + 2*x1 + 17*x3 >= 24, name="healthiness_from_cereal_salads_sashimi")
    model.addConstr(2*x1 + 10*x2 + 17*x3 >= 24, name="healthiness_from_salads_bananas_sashimi")
    model.addConstr(12*x0 + 2*x1 + 10*x2 >= 17, name="healthiness_from_cereal_salads_bananas_2")
    model.addConstr(12*x0 + 2*x1 + 17*x3 >= 17, name="healthiness_from_cereal_salads_sashimi_2")
    model.addConstr(2*x1 + 10*x2 + 17*x3 >= 17, name="healthiness_from_salads_bananas_sashimi_2")
    model.addConstr(12*x0 + 2*x1 + 10*x2 >= 29, name="healthiness_from_cereal_salads_bananas_3")
    model.addConstr(12*x0 + 2*x1 + 17*x3 >= 29, name="healthiness_from_cereal_salads_sashimi_3")
    model.addConstr(2*x1 + 10*x2 + 17*x3 >= 29, name="healthiness_from_salads_bananas_sashimi_3")
    model.addConstr(-2*x0 + 10*x3 >= 0, name="constraint_on_cereal_sashimi")
    model.addConstr(8*x1 + 7*x2 <= 121, name="fiber_from_salads_bananas")
    model.addConstr(3*x0 + 7*x2 <= 122, name="fiber_from_cereal_bananas")
    model.addConstr(3*x0 + 8*x1 + 7*x2 + 15*x3 <= 122, name="fiber_from_all")
    model.addConstr(12*x0 + 2*x1 <= 121, name="healthiness_from_cereal_salads_2")
    model.addConstr(12*x0 + 17*x3 <= 61, name="healthiness_from_cereal_sashimi_2")
    model.addConstr(2*x1 + 10*x2 <= 138, name="healthiness_from_salads_bananas_2")
    model.addConstr(12*x0 + 2*x1 + 17*x3 <= 101, name="healthiness_from_cereal_salads_sashimi_4")
    model.addConstr(12*x0 + 2*x1 + 10*x2 + 17*x3 <= 101, name="healthiness_from_all")

    # Optimize the model
    model.optimize()

    # Print the status of the optimization
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print("Objective value:", model.objVal)
        print("bowls of cereal:", x0.varValue)
        print("bagged salads:", x1.varValue)
        print("bananas:", x2.varValue)
        print("sashimi:", x3.varValue)
    elif model.status == gurobi.GRB.Status.INFEASIBLE:
        print("No feasible solution exists.")
    else:
        print("Optimization failed.")

optimize_problem()
```