## Problem Description and Symbolic Representation

The problem can be broken down into the following components:

- **Variables:**
  - $x_1$ = Number of chocolate cookies
  - $x_2$ = Number of strawberry cookies

- **Objective Function:**
  The profit per chocolate cookie is $1.5, and the profit per strawberry cookie is $1.2. The goal is to maximize the total profit, which can be represented as:
  - Maximize $1.5x_1 + 1.2x_2$

- **Constraints:**
  - The store can make at most 200 cookies total: $x_1 + x_2 \leq 200$
  - They must sell at least 50 chocolate cookies: $x_1 \geq 50$
  - They must sell at least 70 strawberry cookies: $x_2 \geq 70$
  - They can make at most 120 chocolate cookies: $x_1 \leq 120$
  - They can make at most 150 strawberry cookies: $x_2 \leq 150$

## Symbolic Representation in JSON Format

```json
{
  'sym_variables': [('x1', 'chocolate cookies'), ('x2', 'strawberry cookies')],
  'objective_function': '1.5*x1 + 1.2*x2',
  'constraints': [
    'x1 + x2 <= 200',
    'x1 >= 50',
    'x2 >= 70',
    'x1 <= 120',
    'x2 <= 150'
  ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(lb=0, ub=200, name="chocolate_cookies", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(lb=0, ub=200, name="strawberry_cookies", vtype=gurobi.GRB.INTEGER)

    # Objective function
    model.setObjective(1.5 * x1 + 1.2 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 200, name="total_cookies")
    model.addConstr(x1 >= 50, name="min_chocolate_cookies")
    model.addConstr(x2 >= 70, name="min_strawberry_cookies")
    model.addConstr(x1 <= 120, name="max_chocolate_cookies")
    model.addConstr(x2 <= 150, name="max_strawberry_cookies")

    # Optimize model
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Chocolate Cookies: {x1.varValue}")
        print(f"Strawberry Cookies: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_cookie_problem()
```