To solve the optimization problem described, 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 given conditions.

### Symbolic Representation:

Let's define:
- $x_1$ as the number of acres of red grapes grown,
- $x_2$ as the number of acres of green grapes grown.

The objective is to maximize profit. Given that the profit per acre of red grapes is $300 and the profit per acre of green grapes is $250, the objective function can be written as:
\[ \text{Maximize:} \quad 300x_1 + 250x_2 \]

The constraints based on the problem description are:
1. The farmer has 100 acres of land in total: $x_1 + x_2 \leq 100$
2. At least 30 acres of red grapes must be grown: $x_1 \geq 30$
3. At least 25 acres of green grapes must be grown: $x_2 \geq 25$
4. The farmer can grow at most twice the amount of green grapes as red grapes: $x_2 \leq 2x_1$

Thus, the symbolic representation of the problem is:
```json
{
  'sym_variables': [('x1', 'acres of red grapes'), ('x2', 'acres of green grapes')],
  'objective_function': '300*x1 + 250*x2',
  'constraints': ['x1 + x2 <= 100', 'x1 >= 30', 'x2 >= 25', 'x2 <= 2*x1']
}
```

### Gurobi Code:

To solve this optimization problem using Gurobi in Python, we'll use the following code:
```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(300*x1 + 250*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 100, "total_acres")
m.addConstr(x1 >= 30, "min_red_grapes")
m.addConstr(x2 >= 25, "min_green_grapes")
m.addConstr(x2 <= 2*x1, "max_green_to_red_ratio")

# Optimize the model
m.optimize()

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