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 in mathematical terms.

Let's denote:
- \(x_1\) as the number of medical platoons,
- \(x_2\) as the number of signal platoons.

The objective function is to maximize \(6x_1 + 7x_2\).

Given constraints:
1. Deployment weight of medical platoons is 1 metric ton each, and signal platoons is 2 metric tons each.
2. The combined deployment weight must be at least 12 metric tons: \(x_1 + 2x_2 \geq 12\).
3. Seven times the number of medical platoons plus -10 times the number of signal platoons must be at minimum zero: \(7x_1 - 10x_2 \geq 0\).
4. The combined deployment weight must not exceed 25 metric tons: \(x_1 + 2x_2 \leq 25\).
5. Both \(x_1\) and \(x_2\) must be whole numbers.

In symbolic notation, we have:
```json
{
    'sym_variables': [('x1', 'medical platoons'), ('x2', 'signal platoons')],
    'objective_function': '6*x1 + 7*x2',
    'constraints': [
        'x1 + 2*x2 >= 12',
        '7*x1 - 10*x2 >= 0',
        'x1 + 2*x2 <= 25',
        'x1 >= 0', 
        'x2 >= 0',
        'x1 == int(x1)', 
        'x2 == int(x2)'
    ]
}
```

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

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.INTEGER, name="medical_platoons")
x2 = m.addVar(vtype=GRB.INTEGER, name="signal_platoons")

# Set objective function
m.setObjective(6*x1 + 7*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + 2*x2 >= 12, "minimum_deployment_weight")
m.addConstr(7*x1 - 10*x2 >= 0, "platoon_ratio")
m.addConstr(x1 + 2*x2 <= 25, "maximum_deployment_weight")

# Optimize model
m.optimize()

# Print results
for v in m.getVars():
    print("%s %f" % (v.varName, v.x))

print("Obj: %f" % m.objVal)
```