To solve this optimization problem, we first need to define the variables and the objective function in a symbolic representation. Then, we will translate these into Gurobi code.

The variables are:
- `x0`: number of bowls of cereal
- `x1`: number of oranges
- `x2`: number of bowls of pasta

The objective function is to minimize: `8*x0 + 3*x1 + 2*x2`

The constraints based on the problem description are:
1. Carbohydrates from bowls of cereal and bowls of pasta: `6*x0 + 5*x2 >= 21`
2. Total carbohydrates from all sources (first instance): `6*x0 + 3*x1 + 5*x2 >= 34`
3. Total carbohydrates from all sources (second instance, same as above): This is redundant and can be ignored.
4. Calcium from bowls of cereal and bowls of pasta: `4*x0 + x2 >= 37`
5. Calcium from bowls of cereal and oranges: `4*x0 + 4*x1 >= 23`
6. Calcium from oranges and bowls of pasta: `4*x1 + x2 >= 34`
7. Total calcium from all sources (first instance): `4*x0 + 4*x1 + x2 >= 35`
8. Total calcium from all sources (second instance, same as above): This is redundant and can be ignored.
9. Carbohydrates limit from bowls of cereal and bowls of pasta: `6*x0 + 5*x2 <= 48`
10. Total carbohydrates limit from all sources: `6*x0 + 3*x1 + 5*x2 <= 73`

Given the symbolic representation, we can now write this problem in Gurobi code.

```json
{
    'sym_variables': [('x0', 'bowls of cereal'), ('x1', 'oranges'), ('x2', 'bowls of pasta')],
    'objective_function': '8*x0 + 3*x1 + 2*x2',
    'constraints': [
        '6*x0 + 5*x2 >= 21',
        '6*x0 + 3*x1 + 5*x2 >= 34',
        '4*x0 + x2 >= 37',
        '4*x0 + 4*x1 >= 23',
        '4*x1 + x2 >= 34',
        '4*x0 + 4*x1 + x2 >= 35',
        '6*x0 + 5*x2 <= 48',
        '6*x0 + 3*x1 + 5*x2 <= 73'
    ]
}
```

Here's how you can implement this in Gurobi using Python:

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(vtype=GRB.CONTINUOUS, name="bowls_of_cereal")
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="oranges")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="bowls_of_pasta")

# Define the objective function
m.setObjective(8*x0 + 3*x1 + 2*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(6*x0 + 5*x2 >= 21, "carb_cereal_pasta")
m.addConstr(6*x0 + 3*x1 + 5*x2 >= 34, "total_carb_all")
m.addConstr(4*x0 + x2 >= 37, "calcium_cereal_pasta")
m.addConstr(4*x0 + 4*x1 >= 23, "calcium_cereal_oranges")
m.addConstr(4*x1 + x2 >= 34, "calcium_oranges_pasta")
m.addConstr(4*x0 + 4*x1 + x2 >= 35, "total_calcium_all")
m.addConstr(6*x0 + 5*x2 <= 48, "carb_limit_cereal_pasta")
m.addConstr(6*x0 + 3*x1 + 5*x2 <= 73, "total_carb_limit_all")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print('Optimal solution found:')
    for v in m.getVars():
        print(f'{v.varName}: {v.x}')
else:
    print('No optimal solution found')
```