To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining the variables, the objective function, and the constraints using algebraic terms.

### Symbolic Representation:

Let's define the variables as follows:
- $x_0$ represents the number of hamburgers.
- $x_1$ represents the number of oranges.
- $x_2$ represents the number of bowls of instant ramen.

The objective function is to minimize: $6x_0 + 7x_1 + 9x_2$.

The constraints are:
1. Carbohydrates from hamburgers and oranges: $8x_0 + 11x_1 \geq 29$.
2. Carbohydrates from hamburgers and bowls of instant ramen: $8x_0 + x_2 \geq 30$.
3. Total carbohydrates from all sources: $8x_0 + 11x_1 + x_2 \geq 30$.
4. Tastiness rating from hamburgers and oranges: $4x_0 + 7x_1 \geq 37$.
5. Tastiness rating from oranges and bowls of instant ramen: $7x_1 + 9x_2 \geq 39$.
6. Total tastiness rating from all sources (first constraint): $4x_0 + 7x_1 + 9x_2 \geq 22$.
7. Total tastiness rating from all sources (second constraint, essentially the same as the first): $4x_0 + 7x_1 + 9x_2 \geq 22$.
8. Non-negativity and integer constraints: $x_0, x_1, x_2 \in \mathbb{Z}^+$.

Given this symbolic representation, we can now express the problem in a structured format as requested:

```json
{
    'sym_variables': [('x0', 'hamburgers'), ('x1', 'oranges'), ('x2', 'bowls of instant ramen')],
    'objective_function': '6*x0 + 7*x1 + 9*x2',
    'constraints': [
        '8*x0 + 11*x1 >= 29',
        '8*x0 + x2 >= 30',
        '8*x0 + 11*x1 + x2 >= 30',
        '4*x0 + 7*x1 >= 37',
        '7*x1 + 9*x2 >= 39',
        '4*x0 + 7*x1 + 9*x2 >= 22'
    ]
}
```

### Gurobi Code:

To solve this problem using Gurobi, we'll write the Python code to model and solve the optimization problem.

```python
from gurobipy import *

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

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="hamburgers")
x1 = m.addVar(vtype=GRB.INTEGER, name="oranges")
x2 = m.addVar(vtype=GRB.INTEGER, name="bowls_of_instant_ramen")

# Set the objective function
m.setObjective(6*x0 + 7*x1 + 9*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(8*x0 + 11*x1 >= 29, "carbs_hamburgers_oranges")
m.addConstr(8*x0 + x2 >= 30, "carbs_hamburgers_ramen")
m.addConstr(8*x0 + 11*x1 + x2 >= 30, "total_carbs")
m.addConstr(4*x0 + 7*x1 >= 37, "tastiness_hamburgers_oranges")
m.addConstr(7*x1 + 9*x2 >= 39, "tastiness_oranges_ramen")
m.addConstr(4*x0 + 7*x1 + 9*x2 >= 22, "total_tastiness")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"hamburgers: {x0.x}")
    print(f"oranges: {x1.x}")
    print(f"bowls of instant ramen: {x2.x}")
else:
    print("No optimal solution found.")
```