To solve this optimization problem and provide both a symbolic representation of the problem and the corresponding Gurobi code, let's break down the information given:

1. **Variables**: We have two variables - `oranges` and `bowls of cereal`.
2. **Resources/Attributes**:
   - `r0`: Described as 'sourness index' with an upper bound of 45. It has coefficients for oranges (`x0 = 20`) and bowls of cereal (`x1 = 18`).
3. **Objective Function**: Minimize `4.58 * oranges + 4.28 * bowls of cereal`.
4. **Constraints**:
   - The sourness index of oranges is 20.
   - The sourness index of bowls of cereal is 18.
   - The total combined sourness index must be at least 22.
   - Another constraint reiterates that the total combined sourness index must be at least 22 (this seems to be a repetition).
   - `-3 * oranges + 3 * bowls of cereal >= 0`.
   - The total combined sourness index must be at most 33.

Given this information, let's first create a symbolic representation:

- **Variables**: 
  - `x1` represents the amount of `oranges`.
  - `x2` represents the number of `bowls of cereal`.

- **Objective Function**:
  - Minimize `4.58*x1 + 4.28*x2`

- **Constraints**:
  - `20*x1 + 18*x2 >= 22`
  - `-3*x1 + 3*x2 >= 0`
  - `20*x1 + 18*x2 <= 33`

Since the problem allows for fractional numbers of oranges and bowls of cereal, both variables are continuous.

Here is the symbolic representation in JSON format:

```json
{
  'sym_variables': [('x1', 'oranges'), ('x2', 'bowls of cereal')],
  'objective_function': 'Minimize 4.58*x1 + 4.28*x2',
  'constraints': [
    '20*x1 + 18*x2 >= 22',
    '-3*x1 + 3*x2 >= 0',
    '20*x1 + 18*x2 <= 33'
  ]
}
```

Now, let's implement this in Gurobi using Python:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Add variables
x1 = m.addVar(name="oranges", lb=0)  # Assuming non-negativity for amount of oranges
x2 = m.addVar(name="bowls_of_cereal", lb=0)  # Non-negativity for bowls of cereal

# Set the objective function
m.setObjective(4.58*x1 + 4.28*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(20*x1 + 18*x2 >= 22, name="min_sourness")
m.addConstr(-3*x1 + 3*x2 >= 0, name="oranges_vs_cereal")
m.addConstr(20*x1 + 18*x2 <= 33, name="max_sourness")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Oranges: {x1.x}")
    print(f"Bowls of Cereal: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```