## Step 1: Define the symbolic representation of the variables
The variables are 'steaks' and 'bowls of cereal', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $7.99x_1 + 4.73x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
1. $8x_1 + 7x_2 \geq 52$ (at least 52 milligrams of iron from steaks and bowls of cereal),
2. $14x_1 + 7x_2 \geq 60$ (at least 60 milligrams of calcium from steaks and bowls of cereal),
3. $5x_1 - 2x_2 \geq 0$,
4. $8x_1 + 7x_2 \leq 130$ (up to 130 milligrams of iron from steaks and bowls of cereal),
5. $14x_1 + 7x_2 \leq 129$ (at most 129 milligrams of calcium from steaks and bowls of cereal).

## 4: Consider the bounds of the variables
- $x_1$ (steaks) has no integer constraint,
- $x_2$ (bowls of cereal) must be an integer.

## 5: Represent the problem in the required JSON format
```json
{
'sym_variables': [('x1', 'steaks'), ('x2', 'bowls of cereal')],
'objective_function': '7.99*x1 + 4.73*x2',
'constraints': [
    '8*x1 + 7*x2 >= 52',
    '14*x1 + 7*x2 >= 60',
    '5*x1 - 2*x2 >= 0',
    '8*x1 + 7*x2 <= 130',
    '14*x1 + 7*x2 <= 129'
]
}
```

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

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

    # Define the variables
    steaks = model.addVar(name="steaks", lb=0)  # No lower bound specified, assuming 0
    bowls_of_cereal = model.addVar(name="bowls_of_cereal", lb=0, integrality=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(7.99 * steaks + 4.73 * bowls_of_cereal, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(8 * steaks + 7 * bowls_of_cereal >= 52, name="iron_min")
    model.addConstr(14 * steaks + 7 * bowls_of_cereal >= 60, name="calcium_min")
    model.addConstr(5 * steaks - 2 * bowls_of_cereal >= 0, name="steak_cereal_ratio")
    model.addConstr(8 * steaks + 7 * bowls_of_cereal <= 130, name="iron_max")
    model.addConstr(14 * steaks + 7 * bowls_of_cereal <= 129, name="calcium_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Steaks: {steaks.varValue}")
        print(f"Bowls of cereal: {bowls_of_cereal.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```