To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints based on the problem statement.

Let's define:
- $x_1$ as the number of pairs of grey pants.
- $x_2$ as the number of pairs of black pants.

The profit per pair of grey pants is not directly given, but we can infer it from the cost. However, for a complete analysis, let's assume the selling price per pair of grey pants is $G$ and per pair of black pants is $B$. The costs are $25 and $15 respectively. Thus, the profit per pair would be $(G - 25)$ for grey pants and $(B - 15)$ for black pants.

The objective function (to maximize profit) can be written as:
\[ \text{Maximize: } (G - 25)x_1 + (B - 15)x_2 \]

Given that we don't have the exact selling prices, let's assume hypothetical values for illustration: $G = 50$ and $B = 30$. This makes the profit per pair of grey pants $50 - 25 = 25$ dollars and per pair of black pants $30 - 15 = 15$ dollars. Thus, our objective function becomes:
\[ \text{Maximize: } 25x_1 + 15x_2 \]

The constraints based on the processing times are:
1. For Wimo: $40x_1 + 20x_2 \leq 2100$
2. For Webo: $30x_1 + 15x_2 \leq 3000$
3. Non-negativity constraints: $x_1, x_2 \geq 0$

In symbolic notation with natural language objects:
```json
{
    'sym_variables': [('x1', 'number of pairs of grey pants'), ('x2', 'number of pairs of black pants')],
    'objective_function': '25*x1 + 15*x2',
    'constraints': ['40*x1 + 20*x2 <= 2100', '30*x1 + 15*x2 <= 3000', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's implement this in Gurobi using Python:

```python
from gurobipy import *

# Create a model
m = Model("Clothing_Company_Profit")

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="grey_pants", lb=0)
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="black_pants", lb=0)

# Objective function: Maximize profit
m.setObjective(25*x1 + 15*x2, GRB.MAXIMIZE)

# Constraints
m.addConstr(40*x1 + 20*x2 <= 2100, "Wimo_Time")
m.addConstr(30*x1 + 15*x2 <= 3000, "Webo_Time")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Grey Pants: {x1.x}")
    print(f"Black Pants: {x2.x}")
    print(f"Maximum Profit: {m.objVal}")
else:
    print("No optimal solution found")
```