## Step 1: Define the symbolic representation of the problem

Let's denote:
- $x_1$ as the number of large salads
- $x_2$ as the number of small salads

The objective is to maximize profit, where the profit per large salad is $4 and per small salad is $2. Therefore, the objective function can be represented as $4x_1 + 2x_2$.

## Step 2: Define the constraints based on the given resources

The shop has available 1500 g of lettuce and 1200 g of sauce. 
- A large salad takes 45 g of lettuce and 10 g of sauce.
- A small salad takes 30 g of lettuce and 7 g of sauce.

This translates to the following constraints:
1. Lettuce constraint: $45x_1 + 30x_2 \leq 1500$
2. Sauce constraint: $10x_1 + 7x_2 \leq 1200$
Additionally, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of salads cannot be negative.

## 3: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'large salads'), ('x2', 'small salads')],
    'objective_function': '4*x1 + 2*x2',
    'constraints': [
        '45*x1 + 30*x2 <= 1500', 
        '10*x1 + 7*x2 <= 1200', 
        'x1 >= 0', 
        'x2 >= 0'
    ]
}
```

## 4: Convert the problem into Gurobi code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="large_salads", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="small_salads", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Set objective function
    model.setObjective(4 * x1 + 2 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(45 * x1 + 30 * x2 <= 1500, name="lettuce_constraint")
    model.addConstr(10 * x1 + 7 * x2 <= 1200, name="sauce_constraint")

    # Optimize model
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal number of large salads: {x1.varValue}")
        print(f"Optimal number of small salads: {x2.varValue}")
        print(f"Maximal profit: {model.objVal}")
    else:
        print("The problem is infeasible")

solve_salad_shop_problem()
```