To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's define:

- $x_1$ as the number of cream lipsticks produced.
- $x_2$ as the number of matte lipsticks produced.

The objective function is to maximize the total monthly profit, which can be represented as:
\[ 70x_1 + 100x_2 \]

Given the constraints on production time for each process (raw materials preparation, mixing, and packing), we have:

- For raw materials preparation: $3.5x_1 + 5x_2 \leq 300$
- For mixing: $5x_1 + 3x_2 \leq 400$
- For packing: $2x_1 + 1.5x_2 \leq 200$

Additionally, we know that the number of lipsticks produced cannot be negative:
\[ x_1 \geq 0, x_2 \geq 0 \]

The symbolic representation of the problem can be summarized as:

```json
{
    'sym_variables': [('x1', 'cream lipsticks'), ('x2', 'matte lipsticks')], 
    'objective_function': '70*x1 + 100*x2', 
    'constraints': [
        '3.5*x1 + 5*x2 <= 300',
        '5*x1 + 3*x2 <= 400',
        '2*x1 + 1.5*x2 <= 200',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

Now, let's write the Gurobi code to solve this linear programming problem:

```python
from gurobipy import *

# Create a new model
model = Model("Lipstick_Production")

# Define variables
x1 = model.addVar(vtype=GRB.CONTINUOUS, name="cream_lipsticks")
x2 = model.addVar(vtype=GRB.CONTINUOUS, name="matte_lipsticks")

# Set the objective function
model.setObjective(70*x1 + 100*x2, GRB.MAXIMIZE)

# Add constraints
model.addConstr(3.5*x1 + 5*x2 <= 300, "raw_materials")
model.addConstr(5*x1 + 3*x2 <= 400, "mixing")
model.addConstr(2*x1 + 1.5*x2 <= 200, "packing")

# Optimize the model
model.optimize()

# Print the solution
if model.status == GRB.OPTIMAL:
    print(f"Optimal solution: x1 = {x1.x}, x2 = {x2.x}")
    print(f"Maximum profit: {model.objVal}")
else:
    print("No optimal solution found")
```