To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each item (bowls of instant ramen and cheeseburgers), formulating the objective function with these variables, and then listing all constraints in terms of these variables.

Let's denote:
- \(x_0\) as the number of bowls of instant ramen,
- \(x_1\) as the number of cheeseburgers.

The objective function is to minimize: \(1 \times x_0 + 2 \times x_1\).

Constraints are as follows:
1. Iron from bowls of instant ramen and cheeseburgers should be at least 16 milligrams: \(20x_0 + 9x_1 \geq 16\).
2. Calcium from both sources should be at least 45 milligrams: \(11x_0 + 19x_1 \geq 45\).
3. The expression \(-2x_0 + x_1 \geq 0\) must hold.
4. At most 64 milligrams of iron can come from both sources: \(20x_0 + 9x_1 \leq 64\).
5. At most 97 milligrams of calcium can come from both sources: \(11x_0 + 19x_1 \leq 97\).

The symbolic representation is thus:
```json
{
  'sym_variables': [('x0', 'bowls of instant ramen'), ('x1', 'cheeseburgers')],
  'objective_function': '1*x0 + 2*x1',
  'constraints': [
    '20*x0 + 9*x1 >= 16',
    '11*x0 + 19*x1 >= 45',
    '-2*x0 + x1 >= 0',
    '20*x0 + 9*x1 <= 64',
    '11*x0 + 19*x1 <= 97'
  ]
}
```

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

```python
from gurobipy import *

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

# Add variables to the model
x0 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="bowls_of_instant_ramen")
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="cheeseburgers")

# Set the objective function
m.setObjective(1*x0 + 2*x1, GRB.MINIMIZE)

# Add constraints to the model
m.addConstr(20*x0 + 9*x1 >= 16, "Iron_Constraint")
m.addConstr(11*x0 + 19*x1 >= 45, "Calcium_Constraint")
m.addConstr(-2*x0 + x1 >= 0, "Expression_Constraint")
m.addConstr(20*x0 + 9*x1 <= 64, "Iron_Upper_Bound")
m.addConstr(11*x0 + 19*x1 <= 97, "Calcium_Upper_Bound")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"bowls_of_instant_ramen: {x0.x}")
    print(f"cheeseburgers: {x1.x}")
else:
    print("No optimal solution found")
```