To solve the optimization problem described, 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 mathematical notation.

Let's denote:
- \(x_1\) as the quantity of steaks,
- \(x_2\) as the quantity of bowls of cereal.

The objective function is to minimize \(7.99x_1 + 4.73x_2\).

The constraints can be listed as follows:
1. Iron from steaks and bowls of cereal: \(8x_1 + 7x_2 \geq 52\),
2. Same as the first, ensuring we capture the requirement correctly: \(8x_1 + 7x_2 \geq 52\),
3. Calcium from steaks and bowls of cereal: \(14x_1 + 7x_2 \geq 60\),
4. Same as the third, for clarity: \(14x_1 + 7x_2 \geq 60\),
5. Linear constraint: \(5x_1 - 2x_2 \geq 0\),
6. Upper bound of iron: \(8x_1 + 7x_2 \leq 130\),
7. Upper bound of calcium: \(14x_1 + 7x_2 \leq 129\).

Given that \(x_2\) must be an integer (whole number of bowls of cereal) and \(x_1\) can be any real number, we have a mixed-integer linear programming problem.

The symbolic representation in JSON format is:
```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'
    ]
}
```

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

```python
from gurobipy import *

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

# Add variables to the model
x1 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="steaks")
x2 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.INTEGER, name="bowls_of_cereal")

# Set the objective function
m.setObjective(7.99*x1 + 4.73*x2, GRB.MINIMIZE)

# Add constraints to the model
m.addConstr(8*x1 + 7*x2 >= 52, "iron_lower_bound")
m.addConstr(14*x1 + 7*x2 >= 60, "calcium_lower_bound")
m.addConstr(5*x1 - 2*x2 >= 0, "linear_constraint")
m.addConstr(8*x1 + 7*x2 <= 130, "iron_upper_bound")
m.addConstr(14*x1 + 7*x2 <= 129, "calcium_upper_bound")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Steaks: {x1.x}")
    print(f"Bowls of cereal: {x2.x}")
else:
    print("No optimal solution found.")

```