## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'bowls of instant ramen' and 'cheeseburgers'. Let's denote 'bowls of instant ramen' as $x_1$ and 'cheeseburgers' as $x_2$. The objective function to minimize is $1x_1 + 2x_2$. The constraints are:
- $20x_1 + 9x_2 \geq 16$ (iron from both sources)
- $11x_1 + 19x_2 \geq 45$ (calcium from both sources)
- $-2x_1 + x_2 \geq 0$ (relationship between ramen and cheeseburgers)
- $20x_1 + 9x_2 \leq 74$ (upper bound on iron)
- $11x_1 + 19x_2 \leq 104$ (upper bound on calcium, corrected from 97 to 104 to match the given upper bound)

## 2: Convert the problem into a symbolic representation
The symbolic representation is:
- Variables: $x_1$ for 'bowls of instant ramen', $x_2$ for 'cheeseburgers'
- Objective function: Minimize $x_1 + 2x_2$
- Constraints:
  1. $20x_1 + 9x_2 \geq 16$
  2. $11x_1 + 19x_2 \geq 45$
  3. $-2x_1 + x_2 \geq 0$
  4. $20x_1 + 9x_2 \leq 74$
  5. $11x_1 + 19x_2 \leq 104$

## 3: Provide the symbolic representation in the required format
```json
{
'sym_variables': [('x1', 'bowls of instant ramen'), ('x2', 'cheeseburgers')],
'objective_function': 'Minimize x1 + 2x2',
'constraints': [
  '20x1 + 9x2 >= 16',
  '11x1 + 19x2 >= 45',
  '-2x1 + x2 >= 0',
  '20x1 + 9x2 <= 74',
  '11x1 + 19x2 <= 104'
]
}
```

## 4: Implement the problem using Gurobi
```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="bowls_of_instant_ramen", lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name="cheeseburgers", lb=0)  # No lower bound specified, assuming 0

    # Objective function: Minimize 1*x1 + 2*x2
    model.setObjective(x1 + 2*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(20*x1 + 9*x2 >= 16, name="iron_requirement")
    model.addConstr(11*x1 + 19*x2 >= 45, name="calcium_requirement")
    model.addConstr(-2*x1 + x2 >= 0, name="ramen_cheeseburger_relationship")
    model.addConstr(20*x1 + 9*x2 <= 74, name="iron_upper_bound")
    model.addConstr(11*x1 + 19*x2 <= 104, name="calcium_upper_bound")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"bowls of instant ramen: {x1.varValue}")
        print(f"cheeseburgers: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```