To solve this optimization problem using Gurobi, we need to define the decision variables, objective function, and constraints according to the provided description.

### Decision Variables:
- Let $x_0$ be the number of bowls of cereal.
- Let $x_1$ be the number of oranges.
- Let $x_2$ be the number of bowls of pasta.

### Objective Function:
The goal is to minimize $8x_0 + 3x_1 + 2x_2$.

### Constraints:
Based on the problem description, we have several constraints:

1. Carbohydrates from bowls of cereal and bowls of pasta: $6x_0 + 5x_2 \geq 21$
2. Total carbohydrates from all sources: $6x_0 + 3x_1 + 5x_2 \geq 34$ (this constraint is repeated, so we only consider it once)
3. Calcium from bowls of cereal and bowls of pasta: $4x_0 + x_2 \geq 37$
4. Calcium from bowls of cereal and oranges: $4x_0 + 4x_1 \geq 23$
5. Calcium from oranges and bowls of pasta: $4x_1 + x_2 \geq 34$
6. Total calcium from all sources: $4x_0 + 4x_1 + x_2 \geq 35$ (this constraint is repeated, so we only consider it once)
7. Upper bound on carbohydrates from bowls of cereal and bowls of pasta: $6x_0 + 5x_2 \leq 48$
8. Upper bound on total carbohydrates from all sources: $6x_0 + 3x_1 + 5x_2 \leq 73$

Given that the variables can be non-integer (float), we'll model them as continuous variables.

Here is how you could implement this in Gurobi using Python:

```python
from gurobipy import *

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

# Decision 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")

# Objective Function
m.setObjective(8*x0 + 3*x1 + 2*x2, GRB.MINIMIZE)

# Constraints
m.addConstr(6*x0 + 5*x2 >= 21, "carb_cereal_pasta")
m.addConstr(6*x0 + 3*x1 + 5*x2 >= 34, "total_carb")
m.addConstr(4*x0 + x2 >= 37, "calcium_cereal_pasta")
m.addConstr(4*x0 + 4*x1 >= 23, "calcium_cereal_orange")
m.addConstr(4*x1 + x2 >= 34, "calcium_orange_pasta")
m.addConstr(4*x0 + 4*x1 + x2 >= 35, "total_calcium")
m.addConstr(6*x0 + 5*x2 <= 48, "upper_carb_cereal_pasta")
m.addConstr(6*x0 + 3*x1 + 5*x2 <= 73, "upper_total_carb")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"bowls_of_cereal: {x0.x}")
    print(f"oranges: {x1.x}")
    print(f"bowls_of_pasta: {x2.x}")
    print(f"Objective value: {m.objVal}")
else:
    print("No optimal solution found.")
```