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

- $x_1$ as the number of acres for planting tulips,
- $x_2$ as the number of acres for planting orchids.

The objective function is to maximize profit. Given that the profit per acre of tulips is $75 and the profit per acre of orchids is $105, the objective function can be written as:

Maximize: $75x_1 + 105x_2$

The constraints are:
1. The total land used cannot exceed 120 acres: $x_1 + x_2 \leq 120$
2. The budget for seeds cannot exceed $1200, with tulip seeds costing $15 per acre and orchid seeds costing $40 per acre: $15x_1 + 40x_2 \leq 1200$
3. The number of acres for each type of flower cannot be negative: $x_1 \geq 0$, $x_2 \geq 0$

Thus, the symbolic representation of the problem is:

```json
{
    'sym_variables': [('x1', 'acres of tulips'), ('x2', 'acres of orchids')], 
    'objective_function': '75*x1 + 105*x2', 
    'constraints': ['x1 + x2 <= 120', '15*x1 + 40*x2 <= 1200', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's write the Gurobi code in Python to solve this optimization problem:

```python
from gurobipy import *

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

# Create variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="acres_of_tulips")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="acres_of_orchids")

# Set the objective function
m.setObjective(75*x1 + 105*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 120, "total_acres")
m.addConstr(15*x1 + 40*x2 <= 1200, "seed_budget")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Acres of tulips: {x1.x}")
    print(f"Acres of orchids: {x2.x}")
    print(f"Maximum profit: ${75*x1.x + 105*x2.x:.2f}")
else:
    print("No optimal solution found")
```