## Problem Description and Formulation

The problem requires minimizing the objective function \(4.58 \times \text{oranges} + 4.28 \times \text{bowls of cereal}\) subject to several constraints.

### Constraints:

1. **Sourness Index of Oranges and Bowls of Cereal:**
   - The sourness index of oranges (\(r0_{oranges}\)) is 20.
   - The sourness index of bowls of cereal (\(r0_{bowls}\)) is 18.

2. **Total Combined Sourness Index Constraints:**
   - The total combined sourness index must be greater than or equal to 22.
   - The total combined sourness index must be at most 33.

3. **Linear Constraint:**
   - \(-3 \times \text{oranges} + 3 \times \text{bowls of cereal} \geq 0\)

4. **Variable Bounds:**
   - Oranges and bowls of cereal can be fractional.

### Mathematical Formulation:

Let \(x_0\) be the amount of oranges and \(x_1\) be the number of bowls of cereal.

- **Objective Function:** \(\min 4.58x_0 + 4.28x_1\)
- **Constraints:**
  1. \(20x_0 + 18x_1 \geq 22\)
  2. \(20x_0 + 18x_1 \leq 33\)
  3. \(-3x_0 + 3x_1 \geq 0\)
  4. \(x_0, x_1 \geq 0\) (Implicitly assumed as negative amounts do not make sense, but not explicitly bounded below)

### Gurobi Code:

```python
import gurobi

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

    # Define variables
    oranges = model.addVar(name="oranges", lb=0)  # Lower bound is 0, assuming no negative values
    bowls_of_cereal = model.addVar(name="bowls_of_cereal", lb=0)  # Lower bound is 0

    # Objective function
    model.setObjective(4.58 * oranges + 4.28 * bowls_of_cereal, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(20 * oranges + 18 * bowls_of_cereal >= 22, name="sourness_index_min")
    model.addConstr(20 * oranges + 18 * bowls_of_cereal <= 33, name="sourness_index_max")
    model.addConstr(-3 * oranges + 3 * bowls_of_cereal >= 0, name="linear_constraint")

    # Optimize
    model.optimize()

    # Output solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Oranges: {oranges.varValue}")
        print(f"Bowls of Cereal: {bowls_of_cereal.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("The model is infeasible.")

solve_optimization_problem()
```