To solve this optimization problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables and the objective function, followed by the constraints.

### Symbolic Representation

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

- **Objective Function:**
  The profit per chocolate cookie is $1.5, and the profit per strawberry cookie is $1.2. Thus, the objective function to maximize profit is:
  \[ 1.5x_1 + 1.2x_2 \]

- **Constraints:**
  1. Total number of cookies made cannot exceed 200: \( x_1 + x_2 \leq 200 \)
  2. Must sell at least 50 chocolate cookies: \( x_1 \geq 50 \)
  3. Must sell at least 70 strawberry cookies: \( x_2 \geq 70 \)
  4. Can make at most 120 chocolate cookies: \( x_1 \leq 120 \)
  5. Can make at most 150 strawberry cookies: \( x_2 \leq 150 \)

### Symbolic Representation in JSON Format

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

### Gurobi Code in Python

To find the optimal solution, we'll use the Gurobi solver in Python. First, ensure you have Gurobi installed (`pip install gurobipy`) and a valid license.

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(lb=50, ub=120, vtype=GRB.CONTINUOUS, name="chocolate_cookies")
x2 = m.addVar(lb=70, ub=150, vtype=GRB.CONTINUOUS, name="strawberry_cookies")

# Set the objective function
m.setObjective(1.5*x1 + 1.2*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 200, "total_cookies")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Chocolate Cookies: {x1.x}")
    print(f"Strawberry Cookies: {x2.x}")
    print(f"Total Profit: {m.objVal}")
else:
    print("No optimal solution found")
```