## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation.

Let's denote:
- $x_1$ as the number of fridges to be repaired
- $x_2$ as the number of ovens to be repaired

The objective is to maximize earnings, with each fridge earning $100 and each oven earning $125. Therefore, the objective function can be represented as:

Maximize: $100x_1 + 125x_2$

The constraints based on the given time limitations are:
- Inspection time: $20x_1 + 30x_2 \leq 1000$
- Fixing time: $30x_1 + 15x_2 \leq 800$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'fridges'), ('x2', 'ovens')],
    'objective_function': '100*x1 + 125*x2',
    'constraints': [
        '20*x1 + 30*x2 <= 1000',
        '30*x1 + 15*x2 <= 800',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Repairman_Optimization")

# Define variables
x1 = model.addVar(name="fridges", lb=0, vtype=gp.GRB.INTEGER)  # Number of fridges
x2 = model.addVar(name="ovens", lb=0, vtype=gp.GRB.INTEGER)    # Number of ovens

# Objective function: Maximize earnings
model.setObjective(100*x1 + 125*x2, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(20*x1 + 30*x2 <= 1000, name="inspection_time")  # Inspection time constraint
model.addConstr(30*x1 + 15*x2 <= 800, name="fixing_time")      # Fixing time constraint

# Solve the model
model.optimize()

# Print solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
    print(f"Max earnings: ${100*x1.x + 125*x2.x:.2f}")
else:
    print("No optimal solution found.")
```