## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables and the objective function, and then list the constraints.

### Variables
- $x_1$ = Number of winter jackets made daily
- $x_2$ = Number of rain jackets made daily

### Objective Function
The objective is to maximize profit. Given that each winter jacket results in a $5 loss and each rain jacket results in a $50 profit, the objective function can be represented as:
\[ \text{Maximize:} \quad 50x_2 - 5x_1 \]

### Constraints
1. At most 60 winter jackets can be made daily: $x_1 \leq 60$
2. At most 70 rain jackets can be made daily: $x_2 \leq 70$
3. Demand for at least 30 winter jackets daily: $x_1 \geq 30$
4. Demand for at least 35 rain jackets daily: $x_2 \geq 35$
5. Total of at least 80 jackets must be made daily: $x_1 + x_2 \geq 80$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'winter jackets'), ('x2', 'rain jackets')],
    'objective_function': '50*x2 - 5*x1',
    'constraints': [
        'x1 <= 60',
        'x2 <= 70',
        'x1 >= 30',
        'x2 >= 35',
        'x1 + x2 >= 80'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(lb=0, ub=60, name="winter_jackets")
    x2 = model.addVar(lb=0, ub=70, name="rain_jackets")

    # Objective function: Maximize 50*x2 - 5*x1
    model.setObjective(50*x2 - 5*x1, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 >= 30, name="min_winter_jackets")
    model.addConstr(x2 >= 35, name="min_rain_jackets")
    model.addConstr(x1 + x2 >= 80, name="total_jackets")

    # Update model
    model.update()

    # Solve the model
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of winter jackets: {x1.varValue}")
        print(f"Number of rain jackets: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_jacket_problem()
```