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 information provided.

Let's denote:
- $x_1$ as the number of Package A promotions sold.
- $x_2$ as the number of Package B promotions sold.

The objective is to maximize profit. Given that Package A yields a profit of $120 and Package B yields a profit of $200, the objective function can be written as:
\[ \text{Maximize:} \quad 120x_1 + 200x_2 \]

Now, let's define the constraints based on the availability of red and white wines:
- Each Package A requires 2 bottles of red wine, and each Package B also requires 2 bottles of red wine. The company has 1000 bottles of red wine available.
- Each Package A requires 1 bottle of white wine, and each Package B requires 3 bottles of white wine. The company has 800 bottles of white wine available.

Thus, the constraints are:
\[ 2x_1 + 2x_2 \leq 1000 \] (Red wine constraint)
\[ x_1 + 3x_2 \leq 800 \] (White wine constraint)

Additionally, we have non-negativity constraints since the number of packages sold cannot be negative:
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]

In symbolic notation with natural language objects, we have:
```json
{
  'sym_variables': [('x1', 'Number of Package A promotions'), ('x2', 'Number of Package B promotions')],
  'objective_function': '120*x1 + 200*x2',
  'constraints': ['2*x1 + 2*x2 <= 1000', 'x1 + 3*x2 <= 800', 'x1 >= 0', 'x2 >= 0']
}
```

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

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="Package_A")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="Package_B")

# Set the objective function
m.setObjective(120*x1 + 200*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(2*x1 + 2*x2 <= 1000, "Red_Wine_Constraint")
m.addConstr(x1 + 3*x2 <= 800, "White_Wine_Constraint")
m.addConstr(x1 >= 0, "Non_Negativity_x1")
m.addConstr(x2 >= 0, "Non_Negativity_x2")

# Optimize the model
m.optimize()

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